A Comprehensive Review on RNA Subcellular Localization Prediction

Cece Zhang; Jieqiong Wang; Nick Peterson; Shibiao Wan; Xuehuan Zhu

REVIEW 5 major objections 3 minor 196 references

A Comprehensive Review on RNA Subcellular Localization Prediction

T0 review · 5 major / 3 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This review organizes AI-based RNA subcellular localization prediction into sequence-based, image-based, and hybrid methods, and argues hybrid multimodal fusion is the field's most promising next step.

desk verdict A useful but unfinished survey: the taxonomy and tables are solid, yet multiple citations point to unrelated papers, so the review cannot be trusted in its current form. read the letter →

arxiv 2504.17162 v1 pith:3WXOYXCZ submitted 2025-04-24 cs.CV cs.AIq-bio.GNq-bio.SC

Cece Zhang , Xuehuan Zhu , Nick Peterson , Jieqiong Wang , Shibiao Wan This is my paper · ORCID

classification cs.CVcs.AIq-bio.GNq-bio.SC

keywords RNAsubcellularlocalizationlongnon-codingmmicrosequence-basedpredictionimage-basedhybridmultimodalmethodsdeeplearning

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

RNA's job depends on where it sits in the cell, but wet-lab localization is slow and costly. This review surveys AI and machine-learning predictors that instead read RNA sequence, microscopy images, or both, and sorts them into three families: sequence-based, image-based, and hybrid. It argues the sequence-based family is mature and dominant, image-based methods add spatial information but are underused, and hybrid fusion methods are the most promising direction even though none has been implemented yet. The review also identifies data scarcity, imbalanced labels, and missing benchmark datasets as the obstacles that currently cap prediction quality. If it is right, a newcomer can use its tables as a working map of the field and concentrate effort on multimodal fusion rather than another sequence-only variant.

What carries the argument

The organizing device is a three-way taxonomy of methods by input data type: sequence-based, image-based, and hybrid. The taxonomy does the argument's work by turning dozens of separate predictors into three families, each with its own typical features (k-mers, PseKNC, one-hot, physicochemical values for sequences; spatial statistics and raw image tensors for images), algorithms, and failure modes. The companion pipeline—data preparation, feature extraction, feature selection, classification, and single- or multi-label assignment—is the standard template against which each method is described and compared.

What would settle it

Take a random sample of entries from the review's method tables, retrieve each cited paper, and verify that it describes the algorithm, features, and localization set listed; any substantial mismatch, or any established method absent after a systematic search of the same literature, would show the review is not a reliable map.

Watch

Extended reading notes

Core claim

The paper's central claim is that the computational prediction of RNA subcellular localization has converged on a shared pipeline—input data, feature extraction, feature selection, model training, and localization assignment—and that the methods filling that pipeline divide cleanly by input modality. Sequence-based methods, the largest group, rely on k-mer composition, pseudo nucleotide composition, physicochemical features, one-hot encoding, and embeddings, and use algorithms from support vector machines and random forests to CNNs, LSTMs, graph networks, and transformers. Image-based methods extract spatial statistics such as point distributions, Ripley's L-functions, and morphological features from smFISH-style images, or feed preprocessed images directly to a CNN. Hybrid methods that fuse both modalities are described as the natural synthesis, but the paper states that at the time of writing such algorithms had been proposed yet not implemented. The review's own conclusion is that the field's progress is currently limited more by data—small, imbalanced, single-RNA-type datasets and the absence of standardized benchmarks—than by model architecture.

Load-bearing premise

The review's usefulness as a map rests on every bracketed citation pointing to a paper that actually contains the method or result attributed to it; if citation-to-claim matches are wrong, readers can be misled even when the three-way taxonomy is correct.

Editorial extensions

If this is right

A researcher choosing a predictor for mRNA or lncRNA can use the review's method tables as a baseline list; any new sequence-based model should be compared against the families represented there.
Multi-label prediction is the realistic framing, since RNAs often co-localize or shuttle between compartments; single-label results should be read with that limitation in mind.
Image-based approaches are complementary to sequence methods but currently depend on simulated data and costly annotation, so their growth is tied to automated image analysis.
Hybrid multimodal fusion is predicted to be the field's next breakthrough; the review's central gap is that no such system had been published at the time of writing.
Standardized large-scale benchmarks, built from resources like RNALocate and extended beyond fewer-than-ten-thousand mRNA samples, are a necessary condition for reliable comparison.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If hybrid fusion matures, its first decisive test will be whether adding image features to sequence models beats the best sequence-only baselines on the same benchmark; the review does not perform that comparison, so the claim remains a prediction.
The review's taxonomy suggests a concrete research agenda: generate more synthetic smFISH data and automated annotations to relieve the image-data bottleneck, then reuse the well-established sequence encoders as the sequence branch of a fused model.
The emphasis on interpretability in recent deep-learning predictors implies that future methods may be judged not only by accuracy but by whether their attention or gradient scores identify the same cis-regulatory motifs that wet-lab experiments localize.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: the review gives a useful map of a messy field, but the map has wrong labels — several citations point to unrelated papers and one sentence is unfinished. Do not let anyone use it as-is.

The paper organizes RNA subcellular localization prediction into sequence-based, image-based, and hybrid methods, with good tables of predictors and a sensible taxonomy. The introduction covers the biology accurately, and the challenges section (data scarcity, over-reliance on sequence features, missing benchmarks) names real bottlenecks. For a reader new to the area, the structure alone is helpful.

The problems are in the citation integrity. In Section III.B, the text says 'In [14], Clarence et al. applied RF to mRNA localization.' Reference [14] is Jeffery et al. 1983, a wet-lab paper on ascidian embryos. In Section III.C, hybrid frameworks are cited as [188][194], but [188] is Kiskowski et al. on Ripley's K-function and [194] is Huang et al. on breast cancer survival. In Section IV, the claim about lacking paired histopathology images is attributed to 'Wang et al. [188]' — again Kiskowski. These are not interpretive disagreements; the titles and DOIs are unambiguous. In a review, the bibliography is the evidence, and these errors send readers to papers that do not support the sentences. There is also an unresolved placeholder in Section II.B — 'deep neural networks with the attention mechanism were usually preferred because xxx.' That is an unfinished draft, not a stylistic quibble.

The paper is honest that hybrid methods are proposed but not yet implemented, which is fine, but the surrounding text twice suggests they improve prediction without citing an implemented system. That overclaims, though mildly.

Who is this for? A graduate student wanting a bird's-eye view of existing predictors would get value from the tables and the three-way categorization. But they would be misled by the citations in their current state.

Recommendation: reject this version. The taxonomy and tables are worth salvaging; the authors should re-check every reference against the sentence it supports, fill the placeholder, and tone down the hybrid-method enthusiasm. I would support sending a revised version to peer review. If the editor is deciding now, I would not desk-reject outright — the paper's scope is right and a referee can give the authors a concrete fix list — but the manuscript is not publishable in this form.

Referee Report

5 major / 3 minor

Summary. This manuscript is a survey-style review of artificial intelligence and machine learning methods for predicting the subcellular localization of RNAs, including mRNA, lncRNA, and miRNA. The paper organizes methods into three categories: sequence-based, image-based, and hybrid approaches that combine both. It presents two large tables summarizing sequence-based and image-based/hybrid predictors, discusses feature extraction and classification algorithms, and closes with a section on challenges and future directions, including data scarcity, over-reliance on sequence features, and the lack of standardized benchmarks.

Significance. If the review were accurate and complete, it would be a useful entry point for researchers entering the field, because the taxonomy of sequence-, image-, and hybrid-based methods is coherent and the chronological tables cover many recent publications. The manuscript also explicitly identifies open problems such as small dataset sizes, class imbalance, and missing benchmark datasets. However, the central claim of being a reliable, comprehensive map of the literature is undermined by directly verifiable citation-to-claim mismatches in multiple sections, including an unresolved placeholder text in the main body. Since a review's primary value is the trustworthiness of its references, these errors are load-bearing rather than cosmetic.

major comments (5)

[III.B, paragraph on image-based algorithms] The sentence 'In [14], Clarence et al. applied RF to mRNA localization as a multi-label classification problem' cites reference [14], which is Jeffery et al. 1983, 'Localization of actin messenger RNA during early ascidian development' (Dev. Biol. 99:408-417). That is a wet-lab developmental biology paper, not a random-forest image classifier. The described method is the Bento toolkit, which is correctly listed in Table 3 as reference [179]. This mis-citation sends the reader to an unrelated primary source and directly contradicts the review's function as a reliable pointer to the literature.
[III.C, paragraph on hybrid methods] The text states that 'hybrid algorithms like multimodal fusion models [188] and layered neural networks [194] were only proposed but not implemented.' Reference [188] is Kiskowski et al. 2009, a paper on Ripley's K-function for analyzing domain size, and reference [194] is Huang et al. 2019, a breast cancer survival analysis method (SALMON). The actual hybrid frameworks described elsewhere in the paper are [178] (Wang et al. 2023) and [180] (Savulescu et al. 2021). The bracketed citations in this sentence are therefore entirely disconnected from the claims they are supposed to support.
[IV, first paragraph of challenges] The sentence 'as Wang et al. [188] points out, the genome profile data may lack paired histopathology image data for multimodal deep learning approaches to localization prediction' again cites [188], which is Kiskowski et al. 2009 on Ripley's K-function. That paper contains no such claim about genome profile data or histopathology images. This is the same mis-citation appearing in a different context, demonstrating that the problem is not isolated to one sentence but pervades the manuscript's use of references.
[II.B, paragraph on ensemble methods] The text 'Liu et al. in a 2014 study [113] as a hybrid approach that uses both XGBoost and convolutional neural networks' cites reference [113], which is Liu et al. 2014, 'iDNA-Prot|dis: Identifying DNA-Binding Proteins by Incorporating Amino Acid Distance-Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition.' That work is about DNA-binding protein prediction and does not use XGBoost or convolutional neural networks. This is another example of a citation that does not support the attached claim.
[II.B, paragraph on deep learning methods] The sentence 'In many cases [136][142][147][151], deep neural networks with the attention mechanism were usually preferred because xxx' contains an unresolved placeholder 'xxx' and is not a complete sentence. This is not a minor typo; it is an incomplete fragment in the main text that signals the manuscript has not been carefully finalized. The surrounding discussion of attention mechanisms is also not supported by the specific citation numbers, which refer to a mix of papers that do not all focus on attention-based deep networks.

minor comments (3)

[Abstract] The phrase 'RNA s' in the first sentence contains an extra space before the plural 's'; the same pattern appears elsewhere and should be corrected throughout.
[Table 3 caption] The caption states that nuclear envelope 3D labels are used to discriminate distributions 'that are undisguisable' from the 2D view; the intended word is likely 'indistinguishable.'
[III.A, paragraph on image-based features] The phrase 'Ripley's L-functions [188]' is imprecise because [188] specifically discusses the K-function and its derivatives; the text should name the exact quantity to match the cited source.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: this is a literature survey whose claims summarize external published work, with no fitted parameters, derivations, or predictions that reduce to their own inputs.

full rationale

This paper is a narrative review of AI-based RNA subcellular localization prediction. It contains no equations, no fitted parameters, no benchmark experiments, and no claim to derive a prediction from first principles; its central deliverable is a taxonomy of sequence-based, image-based, and hybrid methods with summaries of the cited literature. The review's own descriptions of methods are checkable against the cited primary papers, and the paper explicitly identifies open challenges (Section IV) rather than claiming to have resolved them. Self-citations appear in the introduction (e.g., refs. [73]-[85], protein subcellular localization predictors by Wan et al.) and in ref. [93], a prior review by the same group, but those citations are background context, not load-bearing evidence for any derivation or novel claim; removing them would not change any argument in the review. The detected citation-number mismatches in Sections III.B, III.C, and IV (e.g., attributing a description of Bento to '[14]' when [14] is Jeffery et al. 1983, and citing '[188]' for hybrid frameworks when [188] is Kiskowski et al. 2009) are serious accuracy and reliability problems for a review whose function is to point readers to the correct primary literature, but they are not circularity: the review is not using those citations as premises to derive conclusions that are then claimed as predictions. Similarly, the unfinished placeholder 'deep neural networks with the attention mechanism were usually preferred because xxx' indicates an incomplete sentence and missing support, but it does not constitute a circular derivation. Under the definition of circularity used here — a claimed derivation or prediction that is equivalent, by construction or by self-citation, to its inputs — no such step exists in this manuscript. The appropriate finding is therefore no significant circularity, with the bibliographic integrity concerns flagged separately as correctness risks rather than circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claim of the paper is that it is a comprehensive and reliable review. That claim rests on the accuracy of the citations and the completeness of the surveyed literature, not on mathematical axioms or empirical data. No free parameters or invented entities appear in a literature survey.

assumptions (3)

domain assumption Each bracketed citation refers to a paper that actually supports the sentence to which it is attached.
The review's utility depends on accurate citation-to-claim mapping. This assumption is violated in Section III.B ([14] for Bento), Section III.C ([188][194] for hybrid frameworks), and Section IV ([188] for histopathology data).
domain assumption The surveyed literature is complete and current as of the submission date.
No systematic search protocol or inclusion/exclusion criteria are reported, so 'comprehensive' is assumed rather than demonstrated. Table 2 covers methods through 2024, but completeness is unverifiable from the manuscript.
domain assumption Standard biological background about RNA types and localization mechanisms is accurate.
The introduction draws on textbook biology and general reviews for RNA categories, transport mechanisms, and disease links. These facts are not the paper's contribution and were not independently verified in this report.

how reviews work

0 comments

Cite this review

Pith. "Pith review of A Comprehensive Review on RNA Subcellular Localization Prediction." pith.science (2026). https://pith.science/paper/3WXOYXCZ

@misc{pith2026250417162,
  author       = {Pith},
  title        = {Pith review of: A Comprehensive Review on RNA Subcellular Localization Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3WXOYXCZ}},
  note         = {Machine review of arXiv:2504.17162}
}

read the original abstract

The subcellular localization of RNAs, including long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), microRNAs (miRNAs) and other smaller RNAs, plays a critical role in determining their biological functions. For instance, lncRNAs are predominantly associated with chromatin and act as regulators of gene transcription and chromatin structure, while mRNAs are distributed across the nucleus and cytoplasm, facilitating the transport of genetic information for protein synthesis. Understanding RNA localization sheds light on processes like gene expression regulation with spatial and temporal precision. However, traditional wet lab methods for determining RNA localization, such as in situ hybridization, are often time-consuming, resource-demanding, and costly. To overcome these challenges, computational methods leveraging artificial intelligence (AI) and machine learning (ML) have emerged as powerful alternatives, enabling large-scale prediction of RNA subcellular localization. This paper provides a comprehensive review of the latest advancements in AI-based approaches for RNA subcellular localization prediction, covering various RNA types and focusing on sequence-based, image-based, and hybrid methodologies that combine both data types. We highlight the potential of these methods to accelerate RNA research, uncover molecular pathways, and guide targeted disease treatments. Furthermore, we critically discuss the challenges in AI/ML approaches for RNA subcellular localization, such as data scarcity and lack of benchmarks, and opportunities to address them. This review aims to serve as a valuable resource for researchers seeking to develop innovative solutions in the field of RNA subcellular localization and beyond.

Figures

Figures reproduced from arXiv: 2504.17162 by the authors.

**Figure 2.** Sequence-based approaches for predicting RNA subcellular location. Training data preparation involves techniques aimed at enhancing sequence quality for further use. Data collection focuses on extracting the desired features required for different models. Data cleaning is then performed to select optimal and relevant features from these desired features. The cleaned data is then used for model training. CDHIT: Clus… view at source ↗

**Figure 1.** The overview of machine learning approaches to RNA subcellular localization. The inputs are usually nucleotide sequences in FASTA format, bio-images, or both. Typical features used in sequence-based approaches include sequence composition, nucleotide frequency distribution, signal motifs, etc. Image-based approaches use morphological features, spatial distribution features, etc. Hybrid approaches use a combination o… view at source ↗

**Figure 3.** Three primary categories of computational [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

196 extracted references · 137 canonical work pages

[14]

Localization of actin messenger RNA during early ascidian development,

W. R. Jeffery, C. R. Tomlinson, and R. D. Brodeur, “Localization of actin messenger RNA during early ascidian development,” Dev. Biol., vol. 99, no. 2, pp. 408–417, Oct. 1983, doi: 10.1016/0012-1606(83)90290- 7

work page doi:10.1016/0012-1606(83)90290- 1983
[188]

On the use of Ripley’s K-function and its derivatives to analyze domain size,

M. A. Kiskowski, J. F. Hancock, and A. K. Kenworthy, “On the use of Ripley’s K-function and its derivatives to analyze domain size,” Biophys. J., vol. 97, no. 4, pp. 1095–1103, Aug. 2009, doi: 10.1016/j.bpj.2009.05.039

work page doi:10.1016/j.bpj.2009.05.039 2009
[194]

SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer,

Z. Huang et al., “SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer,” Front. Genet., vol. 10, Mar. 2019, doi: 10.3389/fgene.2019.00166

arXiv 2019
[179]

Bento: a toolkit for subcellular analysis of spatial transcriptomics data,

C. K. Mah et al., “Bento: a toolkit for subcellular analysis of spatial transcriptomics data,” Genome Biol., vol. 25, no. 1, p. 82, Apr. 2024, doi: 10.1186/s13059-024- 03217-7

work page doi:10.1186/s13059-024- 2024
[178]

RNA trafficking and subcellular localization-a review of mechanisms, experimental and predictive methodologies,

J. Wang, M. Horlacher, L. Cheng, and O. Winther, “RNA trafficking and subcellular localization-a review of mechanisms, experimental and predictive methodologies,” Brief. Bioinform., vol. 24, no. 5, p. bbad249, Sep. 2023, doi: 10.1093/bib/bbad249

work page doi:10.1093/bib/bbad249 2023
[180]

Prediction of RNA subcellular localization: Learning from heterogeneous data sources,

A. F. Savulescu, E. Bouilhol, N. Beaume, and M. Nikolski, “Prediction of RNA subcellular localization: Learning from heterogeneous data sources,” iScience, vol. 24, no. 11, p. 103298, Nov. 2021, doi: 10.1016/j.isci.2021.103298

arXiv 2021
[113]

i NA-Prot|dis: Identifying DNA- Binding Proteins by Incorporating Amino Acid Distance- Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition,

B. Liu et al., “i NA-Prot|dis: Identifying DNA- Binding Proteins by Incorporating Amino Acid Distance- Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition,” PLOS ONE, vol. 9, no. 9, p. e106691, Sep. 2014, doi: 10.1371/journal.pone.0106691

work page doi:10.1371/journal.pone.0106691 2014
[136]

MulStack: An ensemble learning prediction model of multilabel mRNA subcellular localization,

Z. Liu, T. Bai, B. Liu, and L. Yu, “MulStack: An ensemble learning prediction model of multilabel mRNA subcellular localization,” Comput. Biol. Med., vol. 175, p. 108289, Jun. 2024, doi: 10.1016/j.compbiomed.2024.108289

arXiv 2024
[142]

Allocator is a graph neural network-based framework for mRNA subcellular localization prediction,

F. Li, Y. Bi, X. Guo, X. Tan, C. Wang, and S. Pan, “Allocator is a graph neural network-based framework for mRNA subcellular localization prediction,” ec. 15, 2023, bioRxiv. doi: 10.1101/2023.12.14.571762

work page doi:10.1101/2023.12.14.571762 2023
[147]

LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism,

M. Zeng et al., “LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism,” Bioinformatics, vol. 39, no. 12, p. btad752, Dec. 2023, doi: 10.1093/bioinformatics/btad752

work page doi:10.1093/bioinformatics/btad752 2023
[151]

EL-RMLocNet: An explainable LSTM network for RNA-associated multi-compartment localization prediction,

M. N. Asim et al., “EL-RMLocNet: An explainable LSTM network for RNA-associated multi-compartment localization prediction,” Comput. Struct. Biotechnol. J., vol. 20, pp. 3986–4002, 2022, doi: 10.1016/j.csbj.2022.07.031

work page doi:10.1016/j.csbj.2022.07.031 2022

Show all 196 references

[1]

Systematics for types and effects of RNA variations,

M. Vihinen, “Systematics for types and effects of RNA variations,” RNA Biol., vol. 18, no. 4, pp. 481–498, Apr. 2021, doi: 10.1080/15476286.2020.1817266

2021
[2]

RNAseq by Total RNA Library Identifies Additional RNAs Compared to Poly(A) RNA Library,

Y. Guo et al., “RNAseq by Total RNA Library Identifies Additional RNAs Compared to Poly(A) RNA Library,” BioMed Res. Int., vol. 2015, p. 862130, 2015, doi: 10.1155/2015/862130

2015 doi
[3]

Translational control mechanisms in metabolic regulation: critical role of RNA binding proteins, microRNAs, and cytoplasmic RNA granules,

K. Adeli, “Translational control mechanisms in metabolic regulation: critical role of RNA binding proteins, microRNAs, and cytoplasmic RNA granules,” Am. J. Physiol. Endocrinol. Metab., vol. 301, no. 6, pp. E1051- 1064, Dec. 2011, doi: 10.1152/ajpendo.00399.2011

2011
[4]

Multifunctional regulatory proteins that control gene expression in both the nucleus and the cytoplasm,

M. F. Wilkinson and A. B. Shyu, “Multifunctional regulatory proteins that control gene expression in both the nucleus and the cytoplasm,” BioEssays News Rev. Mol. Cell. Dev. Biol., vol. 23, no. 9, pp. 775–787, Sep. 2001, doi: 10.1002/bies.1113

2001 doi
[5]

The ends of the affair: capping and polyadenylation,

A. J. Shatkin and J. L. Manley, “The ends of the affair: capping and polyadenylation,” Nat. Struct. Biol., vol. 7, no. 10, pp. 838–842, Oct. 2000, doi: 10.1038/79583

doi
[6]

Mitochondrial-specific aminoacyl-RNA synthetases.,

W. E. Barnett, D. H. Brown, and J. L. Epler, “Mitochondrial-specific aminoacyl-RNA synthetases.,” Proc. Natl. Acad. Sci., vol. 57, no. 6, pp. 1775–1781, Jun. 1967, doi: 10.1073/pnas.57.6.1775

1967 doi
[7]

Nuclear Photosynthetic Gene Expression Is Synergistically Modulated by Rates of Protein Synthesis in Chloroplasts and Mitochondria,

P. Pesaresi et al., “Nuclear Photosynthetic Gene Expression Is Synergistically Modulated by Rates of Protein Synthesis in Chloroplasts and Mitochondria,” Plant Cell, vol. 18, no. 4, pp. 970–991, Apr. 2006, doi: 10.1105/tpc.105.039073

2006 doi
[8]

Reovirus mRNA: transcription and translation,

A. J. Shatkin and G. W. Both, “Reovirus mRNA: transcription and translation,” Cell, vol. 7, no. 3, pp. 305– 313, Mar. 1976, doi: 10.1016/0092-8674(76)90159-8

1976 doi
[9]

So close, no matter how far: multiple paths connecting transcription to mRNA translation in eukaryotes,

B. Slobodin and R. ikstein, “So close, no matter how far: multiple paths connecting transcription to mRNA translation in eukaryotes,” EMBO Rep., vol. 21, no. 9, p. e50799, Sep. 2020, doi: 10.15252/embr.202050799

2020 doi
[10]

Regulation of Protein Synthesis by mRNA Structure - Research Projects - Ermolenko Lab - University of Rochester Medical Center

“Regulation of Protein Synthesis by mRNA Structure - Research Projects - Ermolenko Lab - University of Rochester Medical Center.” Accessed: Nov. 22, 2024. [Online]. Available: https://www.urmc.rochester.edu/labs/ermolenko/projects/ regulation-of-protein-synthesis-by-mrna-struc...

2024
[11]

The role of mRNA and protein stability in gene expression,

J. L. Hargrove and F. H. Schmidt, “The role of mRNA and protein stability in gene expression,” FASEB J. Off. Publ. Fed. Am. Soc. Exp. Biol., vol. 3, no. 12, pp. 2360– 2370, Oct. 1989, doi: 10.1096/fasebj.3.12.2676679

1989 doi
[12]

Mechanisms of subcellular mRNA localization,

M. Kloc, N. R. Zearfoss, and L. . Etkin, “Mechanisms of subcellular mRNA localization,” Cell, vol. 108, no. 4, pp. 533–544, Feb. 2002, doi: 10.1016/s0092- 8674(02)00651-7

2002 doi
[13]

mRNA localization: gene expression in the spatial dimension,

K. C. Martin and A. Ephrussi, “mRNA localization: gene expression in the spatial dimension,” Cell, vol. 136, no. 4, pp. 719–730, Feb. 2009, doi: 10.1016/j.cell.2009.01.044

2009 doi
[15]

Intracellular localization of messenger RNAs for cytoskeletal proteins,

J. B. Lawrence and R. H. Singer, “Intracellular localization of messenger RNAs for cytoskeletal proteins,” Cell, vol. 45, no. 3, pp. 407–415, May 1986, doi: 10.1016/0092-8674(86)90326-0

1986 doi
[16]

Subcellular mRNA localization in animal cells and why it matters,

C. E. Holt and S. L. Bullock, “Subcellular mRNA localization in animal cells and why it matters,” Science, vol. 326, no. 5957, pp. 1212–1216, Nov. 2009, doi: 10.1126/science.1176488

2009 doi
[17]

Principles and roles of mRNA localization in animal development,

C. Medioni, K. Mowry, and F. Besse, “Principles and roles of mRNA localization in animal development,” Dev. Camb. Engl., vol. 139, no. 18, pp. 3263–3276, Sep. 2012, doi: 10.1242/dev.078626

2012 doi
[18]

Intracellular mRNA transport and localized translation,

S. Das, M. Vera, V. Gandin, R. H. Singer, and E. Tutucci, “Intracellular mRNA transport and localized translation,” Nat. Rev. Mol. Cell Biol., vol. 22, no. 7, pp. 483–504, Jul. 2021, doi: 10.1038/s41580-021-00356-8

2021 doi
[19]

The functions and regulatory principles of mRNA intracellular trafficking,

J. Bergalet and E. Lécuyer, “The functions and regulatory principles of mRNA intracellular trafficking,” Adv. Exp. Med. Biol., vol. 825, pp. 57–96, 2014, doi: 10.1007/978- 1-4939-1221-6_2

2014 doi
[20]

Identification of a cis-acting element that localizes mRNA to synapses,

E. J. Meer, D. O. Wang, S. Kim, I. Barr, F. Guo, and K. C. Martin, “Identification of a cis-acting element that localizes mRNA to synapses,” Proc. Natl. Acad. Sci., vol. 109, no. 12, pp. 4639–4644, Mar. 2012, doi: 10.1073/pnas.1116269109

2012 doi
[21]

mRNA-based protein targeting to the endoplasmic reticulum and chloroplasts in plant cells,

L. Tian and T. W. Okita, “mRNA-based protein targeting to the endoplasmic reticulum and chloroplasts in plant cells,” Curr. Opin. Plant Biol., vol. 22, pp. 77–85, Dec. 2014, doi: 10.1016/j.pbi.2014.09.007

2014 doi
[22]

Translational control of localized mRNAs: restricting protein synthesis in space and time,

F. Besse and A. Ephrussi, “Translational control of localized mRNAs: restricting protein synthesis in space and time,” Nat. Rev. Mol. Cell Biol., vol. 9, no. 12, pp. 971–980, Dec. 2008, doi: 10.1038/nrm2548

2008 doi
[23]

Transmitting the message: intracellular mRNA localization,

C. Meignin and I. avis, “Transmitting the message: intracellular mRNA localization,” Curr. Opin. Cell Biol., vol. 22, no. 1, pp. 112–119, Feb. 2010, doi: 10.1016/j.ceb.2009.11.011

2010 doi
[24]

Zipcodes and postage stamps: mRNA localisation signals and their trans-acting binding proteins,

H. Chabanon, I. Mickleburgh, and J. Hesketh, “Zipcodes and postage stamps: mRNA localisation signals and their trans-acting binding proteins,” Brief. Funct. Genomic. Proteomic., vol. 3, no. 3, pp. 240–256, Nov. 2004, doi: 10.1093/bfgp/3.3.240

2004 doi
[25]

CLIP and complementary methods,

M. Hafner et al., “CLIP and complementary methods,” Nat. Rev. Methods Primer, vol. 1, no. 1, pp. 1–23, Mar. 2021, doi: 10.1038/s43586-021-00018-1

2021 doi
[26]

Global Positioning System: Understanding Long Noncoding RNAs through Subcellular Localization,

J. Carlevaro-Fita and R. Johnson, “Global Positioning System: Understanding Long Noncoding RNAs through Subcellular Localization,” Mol. Cell, vol. 73, no. 5, pp. 869–883, Mar. 2019, doi: 10.1016/j.molcel.2019.02.008

2019 doi
[27]

Transcriptional and Post- transcriptional Gene Regulation by Long Non-coding RNA,

I. M. ykes and C. Emanueli, “Transcriptional and Post- transcriptional Gene Regulation by Long Non-coding RNA,” Genomics Proteomics Bioinformatics, vol. 15, no. 3, pp. 177–186, Jun. 2017, doi: 10.1016/j.gpb.2016.12.005

2017 doi
[28]

esign powerful predictor for mRNA subcellular location prediction in Homo sapiens,

Z.-Y. Zhang, Y.-H. Yang, H. Ding, D. Wang, W. Chen, and H. Lin, “ esign powerful predictor for mRNA subcellular location prediction in Homo sapiens,” Brief. Bioinform., vol. 22, no. 1, pp. 526–535, Jan. 2021, doi: 10.1093/bib/bbz177

2021 doi
[29]

The Epigenome in Neurodevelopmental isorders,

J. Reichard and G. Zimmer-Bensch, “The Epigenome in Neurodevelopmental isorders,” Front. Neurosci., vol. 15, p. 776809, 2021, doi: 10.3389/fnins.2021.776809

2021
[30]

Brain Organoids and Long Non-coding RNAs: Neuroprotective Pathways in Alcohol-Induced Developmental Neurotoxicity - ProQuest

“Brain Organoids and Long Non-coding RNAs: Neuroprotective Pathways in Alcohol-Induced Developmental Neurotoxicity - ProQuest.” Accessed: Nov. 22, 2024. [Online]. Available: https://www.proquest.com/docview/2621683760

2024
[31]

Molecular characterisation of the transcription factor – chromatin landscape interplay during neuronal cell fate acquisition,

J. Jung, “Molecular characterisation of the transcription factor – chromatin landscape interplay during neuronal cell fate acquisition,” Johannes Gutenberg-Universität Mainz, 2018. doi: 10.25358/OPENSCIENCE-3841

2018 doi
[32]

LncRNA SNHG1 enhances cell proliferation, migration, and invasion in cervical cancer,

Y. Liu et al., “LncRNA SNHG1 enhances cell proliferation, migration, and invasion in cervical cancer,” Biochem. Cell Biol. Biochim. Biol. Cell., vol. 96, no. 1, pp. 38–43, Feb. 2018, doi: 10.1139/bcb-2017-0188

2018 doi
[33]

Reduced LHFPL3-AS2 lncRNA expression is linked to altered epithelial polarity and proliferation, and to ileal ulceration in Crohn disease,

K. E. Sosnovski et al., “Reduced LHFPL3-AS2 lncRNA expression is linked to altered epithelial polarity and proliferation, and to ileal ulceration in Crohn disease,” Sci. Rep., vol. 13, no. 1, p. 20513, Nov. 2023, doi: 10.1038/s41598-023-47997-7

2023 doi
[34]

The RNA-centred view of the synapse: non-coding RNAs and synaptic plasticity,

N. R. Smalheiser, “The RNA-centred view of the synapse: non-coding RNAs and synaptic plasticity,” Philos. Trans. R. Soc. B Biol. Sci., vol. 369, no. 1652, p. 20130504, Sep. 2014, doi: 10.1098/rstb.2013.0504

2014
[35]

Understanding lncRNA–protein assemblies with imaging and single- molecule approaches,

J. Liu, L.-Z. Yang, and L.-L. Chen, “Understanding lncRNA–protein assemblies with imaging and single- molecule approaches,” Curr. Opin. Genet. Dev., vol. 72, pp. 128–137, Feb. 2022, doi: 10.1016/j.gde.2021.11.005

2022 doi
[36]

Long non-coding RNAs in human early embryonic development and their potential in ART,

J. Bouckenheimer et al., “Long non-coding RNAs in human early embryonic development and their potential in ART,” Hum. Reprod. Update, vol. 23, no. 1, pp. 19– 40, Dec. 2016, doi: 10.1093/humupd/dmw035

2016 doi
[37]

A novel role for lncRNAs in cell cycle control during stress adaptation,

C. Solé, M. Nadal-Ribelles, E. de Nadal, and F. Posas, “A novel role for lncRNAs in cell cycle control during stress adaptation,” Curr. Genet., vol. 61, no. 3, pp. 299–308, 2015, doi: 10.1007/s00294-014-0453-y

2015 doi
[38]

Long Noncoding RNAs in Human Stemness and ifferentiation,

F. Mirzadeh Azad, I. L. Polignano, V. Proserpio, and S. Oliviero, “Long Noncoding RNAs in Human Stemness and ifferentiation,” Trends Cell Biol., vol. 31, no. 7, pp. 542–555, Jul. 2021, doi: 10.1016/j.tcb.2021.02.002

2021 doi
[39]

The decalog of long non-coding RNA involvement in cancer diagnosis and monitoring,

T. Kunej, J. Obsteter, Z. Pogacar, S. Horvat, and G. A. Calin, “The decalog of long non-coding RNA involvement in cancer diagnosis and monitoring,” Crit. Rev. Clin. Lab. Sci., vol. 51, no. 6, pp. 344–357, Dec. 2014, doi: 10.3109/10408363.2014.944299

2014
[40]

LncRNAs: From Basic Research to Medical Application,

T. Wu and Y. u, “LncRNAs: From Basic Research to Medical Application,” Int. J. Biol. Sci., vol. 13, no. 3, pp. 295–307, 2017, doi: 10.7150/ijbs.16968

2017 doi
[41]

Exploiting Long Noncoding RNAs as Pharmacological Targets to Modulate Epigenetic iseases,

B. Prabhakar, X.-B. Zhong, and T. P. Rasmussen, “Exploiting Long Noncoding RNAs as Pharmacological Targets to Modulate Epigenetic iseases,” Yale J. Biol. Med., vol. 90, no. 1, pp. 73–86, Mar. 2017

2017
[42]

The risk-associated long noncoding RNA NBAT-1 controls neuroblastoma progression by regulating cell proliferation and neuronal differentiation,

G. K. Pandey et al., “The risk-associated long noncoding RNA NBAT-1 controls neuroblastoma progression by regulating cell proliferation and neuronal differentiation,” Cancer Cell, vol. 26, no. 5, pp. 722–737, Nov. 2014, doi: 10.1016/j.ccell.2014.09.014

2014 doi
[43]

Long non-coding RNAs in development and disease: conservation to mechanisms,

I. Tsagakis, K. ouka, I. Birds, and J. L. Aspden, “Long non-coding RNAs in development and disease: conservation to mechanisms,” J. Pathol., vol. 250, no. 5, pp. 480–495, Apr. 2020, doi: 10.1002/path.5405

2020 doi
[44]

Functional roles and networks of non-coding RNAs in the pathogenesis of neurodegenerative diseases,

Y.-Y. Wu and H.-C. Kuo, “Functional roles and networks of non-coding RNAs in the pathogenesis of neurodegenerative diseases,” J. Biomed. Sci., vol. 27, no. 1, p. 49, Apr. 2020, doi: 10.1186/s12929-020-00636-z

2020 doi
[45]

T P-43 Proteinopathy and ALS: Insights into Disease Mechanisms and Therapeutic Targets,

E. L. Scotter, H.-J. Chen, and C. E. Shaw, “T P-43 Proteinopathy and ALS: Insights into Disease Mechanisms and Therapeutic Targets,” Neurother. J. Am. Soc. Exp. Neurother., vol. 12, no. 2, pp. 352–363, Apr. 2015, doi: 10.1007/s13311-015-0338-x

2015 doi
[46]

The research progress of LncRNA involved in the regulation of inflammatory diseases,

K. Liao, J. Xu, W. Yang, X. You, Q. Zhong, and X. Wang, “The research progress of LncRNA involved in the regulation of inflammatory diseases,” Mol. Immunol., vol. 101, pp. 182–188, Sep. 2018, doi: 10.1016/j.molimm.2018.05.030

2018 doi
[47]

Long noncoding RNAs: novel links to inflammatory bowel disease?,

L. E. Edgington-Mitchell, “Long noncoding RNAs: novel links to inflammatory bowel disease?,” Am. J. Physiol. Gastrointest. Liver Physiol., vol. 311, no. 3, pp. G444- 445, Sep. 2016, doi: 10.1152/ajpgi.00271.2016

2016
[48]

The contribution of long non- coding RNAs in Inflammatory Bowel iseases,

E. Zacharopoulou, M. Gazouli, M. Tzouvala, A. Vezakis, and G. Karamanolis, “The contribution of long non- coding RNAs in Inflammatory Bowel iseases,” Dig. Liver Dis. Off. J. Ital. Soc. Gastroenterol. Ital. Assoc. Study Liver, vol. 49, no. 10, pp. 1067–1072, Oct. 2017, doi: 10....

2017 doi
[49]

The role of lncRNAs in hepatocellular carcinoma: opportunities as novel targets for pharmacological intervention,

H. Zou et al., “The role of lncRNAs in hepatocellular carcinoma: opportunities as novel targets for pharmacological intervention,” Expert Rev. Gastroenterol. Hepatol., vol. 10, no. 3, pp. 331–340, 2016, doi: 10.1586/17474124.2016.1116382

2016
[50]

Molecular biology: the expanding world of small RNAs,

H. Grosshans and W. Filipowicz, “Molecular biology: the expanding world of small RNAs,” Nature, vol. 451, no. 7177, pp. 414–416, Jan. 2008, doi: 10.1038/451414a

2008 doi
[51]

Regulation by small RNAs in bacteria: expanding frontiers,

G. Storz, J. Vogel, and K. M. Wassarman, “Regulation by small RNAs in bacteria: expanding frontiers,” Mol. Cell, vol. 43, no. 6, pp. 880–891, Sep. 2011, doi: 10.1016/j.molcel.2011.08.022

2011 doi
[52]

Small regulatory RNAs in Archaea,

J. Babski et al., “Small regulatory RNAs in Archaea,” RNA Biol., vol. 11, no. 5, p. 484, Mar. 2014, doi: 10.4161/rna.28452

2014 doi
[53]

Computational approaches for microRNA studies: a review,

L. Li, J. Xu, D. Yang, X. Tan, and H. Wang, “Computational approaches for microRNA studies: a review,” Mamm. Genome Off. J. Int. Mamm. Genome Soc., vol. 21, no. 1–2, pp. 1–12, Feb. 2010, doi: 10.1007/s00335-009-9241-2

2010 doi
[54]

Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs,

M. N. Asim, M. A. Ibrahim, M. Imran Malik, A. Dengel, and S. Ahmed, “Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs,” Int. J. Mol. Sci., vol. 22, no. 16, p. 8719, Aug. 2021, doi: 10.3390/ijms22168719

2021 doi
[55]

Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach: application of Boruta,

F. Hamidi et al., “Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach: application of Boruta,” Front. Digit. Health, vol. 5, Aug. 2023, doi: 10.3389/fdgth.2023.1187578

2023
[56]

Identifying MicroRNA Markers That Predict COVI -19 Severity Using Machine Learning Methods,

J. Ren, W. Guo, K. Feng, T. Huang, and Y. Cai, “Identifying MicroRNA Markers That Predict COVI -19 Severity Using Machine Learning Methods,” Life Basel Switz., vol. 12, no. 12, p. 1964, Nov. 2022, doi: 10.3390/life12121964

1964 doi
[57]

HLGNN-MDA: Heuristic Learning Based on Graph Neural Networks for miRNA- isease Association Prediction,

L. Yu, B. Ju, and S. Ren, “HLGNN-MDA: Heuristic Learning Based on Graph Neural Networks for miRNA- isease Association Prediction,” Int. J. Mol. Sci., vol. 23, no. 21, p. 13155, Oct. 2022, doi: 10.3390/ijms232113155

2022 doi
[58]

22, 2024

“ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler | Briefings in Bioinformatics | Oxford Academic.” Accessed: Nov. 22, 2024. [Online]. Available: https://academic.oup.com/bib/a...

2024
[59]

Regulation of mRNA transport, localization and translation in the nervous system of mammals (Review),

C. M. D. Liegro, G. Schiera, and I. D. Liegro, “Regulation of mRNA transport, localization and translation in the nervous system of mammals (Review),” Int. J. Mol. Med., vol. 33, no. 4, p. 747, Jan. 2014, doi: 10.3892/ijmm.2014.1629

2014
[60]

Mutational landscape of RNA-binding proteins in human cancers,

Y. Neelamraju, A. Gonzalez-Perez, P. Bhat-Nakshatri, H. Nakshatri, and S. C. Janga, “Mutational landscape of RNA-binding proteins in human cancers,” RNA Biol., vol. 15, no. 1, p. 115, Nov. 2017, doi: 10.1080/15476286.2017.1391436

2017
[61]

Melanoma addiction to the long non- coding RNA SAMMSON,

E. Leucci et al., “Melanoma addiction to the long non- coding RNA SAMMSON,” Nature, vol. 531, no. 7595, pp. 518–522, Mar. 2016, doi: 10.1038/nature17161

2016 doi
[62]

A comprehensive expression landscape of RNA-binding proteins (RBPs) across 16 human cancer types,

B. Zhang et al., “A comprehensive expression landscape of RNA-binding proteins (RBPs) across 16 human cancer types,” RNA Biol., vol. 17, no. 2, pp. 211–226, Feb. 2020, doi: 10.1080/15476286.2019.1673657

2020
[63]

Overexpression of ribosomal RNA in prostate cancer is common but not linked to rDNA promoter hypomethylation,

M. Uemura, Q. Zheng, C. M. Koh, W. G. Nelson, S. Yegnasubramanian, and A. M. De Marzo, “Overexpression of ribosomal RNA in prostate cancer is common but not linked to rDNA promoter hypomethylation,” Oncogene, vol. 31, no. 10, pp. 1254– 1263, Mar. 2012, doi: 10.1038/onc.2011.319

2012 doi
[64]

iagnostic and prognostic implications of ribosomal protein transcript expression patterns in human cancers,

J. M. Dolezal, A. P. Dash, and E. V. Prochownik, “ iagnostic and prognostic implications of ribosomal protein transcript expression patterns in human cancers,” BMC Cancer, vol. 18, no. 1, p. 275, Mar. 2018, doi: 10.1186/s12885-018-4178-z

2018 doi
[65]

RNA and isease,

T. A. Cooper, L. Wan, and G. reyfuss, “RNA and isease,” Cell, vol. 136, no. 4, p. 777, Feb. 2009, doi: 10.1016/j.cell.2009.02.011

2009 doi
[66]

Endoplasmic reticulum stress and inflammation in the central nervous system,

N. T. Sprenkle, S. G. Sims, C. L. Sánchez, and G. P. Meares, “Endoplasmic reticulum stress and inflammation in the central nervous system,” Mol. Neurodegener., vol. 12, no. 1, p. 42, May 2017, doi: 10.1186/s13024-017- 0183-y

2017 doi
[67]

Mutations in mRNA export mediator GLE1 result in a fetal motoneuron disease,

H. O. Nousiainen et al., “Mutations in mRNA export mediator GLE1 result in a fetal motoneuron disease,” Nat. Genet., vol. 40, no. 2, pp. 155–157, Feb. 2008, doi: 10.1038/ng.2007.65

2008 doi
[68]

M. Okamura et al., “ epletion of mRNA export regulator DBP5/DDX19, GLE1 or IPPK that is a key enzyme for the production of IP6, resulting in differentially altered cytoplasmic mRNA expression and specific cell defect,” PloS One, vol. 13, no. 5, p. e0197165, 2018, doi: 10.1371/...

2018 doi
[69]

Fragile X syndrome: loss of local mRNA regulation alters synaptic development and function,

G. J. Bassell and S. T. Warren, “Fragile X syndrome: loss of local mRNA regulation alters synaptic development and function,” Neuron, vol. 60, no. 2, pp. 201–214, Oct. 2008, doi: 10.1016/j.neuron.2008.10.004

2008 doi
[70]

A direct role for FMRP in activity-dependent dendritic mRNA transport links filopodial-spine morphogenesis to fragile X syndrome,

J. B. Dictenberg, S. A. Swanger, L. N. Antar, R. H. Singer, and G. J. Bassell, “A direct role for FMRP in activity-dependent dendritic mRNA transport links filopodial-spine morphogenesis to fragile X syndrome,” Dev. Cell, vol. 14, no. 6, pp. 926–939, Jun. 2008, doi: 10.1016/j....

2008 doi
[71]

Nuclear Localization of Huntingtin mRNA Is Specific to Cells of Neuronal Origin,

M.-C. Didiot et al., “Nuclear Localization of Huntingtin mRNA Is Specific to Cells of Neuronal Origin,” Cell Rep., vol. 24, no. 10, pp. 2553-2560.e5, Sep. 2018, doi: 10.1016/j.celrep.2018.07.106

2018 doi
[72]

Local RNA translation at the synapse and in disease,

L. Liu-Yesucevitz et al., “Local RNA translation at the synapse and in disease,” J. Neurosci. Off. J. Soc. Neurosci., vol. 31, no. 45, pp. 16086–16093, Nov. 2011, doi: 10.1523/JNEUROSCI.4105-11.2011

2011 doi
[73]

GOASVM: a subcellular location predictor by incorporating term- frequency gene ontology into the general form of Chou’s pseudo-amino acid composition,

S. Wan, M.-W. Mak, and S.-Y. Kung, “GOASVM: a subcellular location predictor by incorporating term- frequency gene ontology into the general form of Chou’s pseudo-amino acid composition,” J. Theor. Biol., vol. 323, pp. 40–48, Apr. 2013, doi: 10.1016/j.jtbi.2013.01.012

2013 doi
[74]

mGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machines,

S. Wan, M.-W. Mak, and S.-Y. Kung, “mGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machines,” BMC Bioinformatics, vol. 13, no. 1, p. 290, Nov. 2012, doi: 10.1186/1471-2105-13-290

2012 doi
[75]

R3P-Loc: a compact multi-label predictor using ridge regression and random projection for protein subcellular localization,

S. Wan, M.-W. Mak, and S.-Y. Kung, “R3P-Loc: a compact multi-label predictor using ridge regression and random projection for protein subcellular localization,” J. Theor. Biol., vol. 360, pp. 34–45, Nov. 2014, doi: 10.1016/j.jtbi.2014.06.031

2014 doi
[76]

mPLR-Loc: An adaptive decision multi-label classifier based on penalized logistic regression for protein subcellular localization prediction,

S. Wan, M.-W. Mak, and S.-Y. Kung, “mPLR-Loc: An adaptive decision multi-label classifier based on penalized logistic regression for protein subcellular localization prediction,” Anal. Biochem., vol. 473, pp. 14–27, Mar. 2015, doi: 10.1016/j.ab.2014.10.014

2015 doi
[77]

HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins,

S. Wan, M.-W. Mak, and S.-Y. Kung, “HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins,” PloS One, vol. 9, no. 3, p. e89545, 2014, doi: 10.1371/journal.pone.0089545

2014 doi
[78]

FUEL-mLoc: feature-unified prediction and explanation of multi- localization of cellular proteins in multiple organisms,

S. Wan, M.-W. Mak, and S.-Y. Kung, “FUEL-mLoc: feature-unified prediction and explanation of multi- localization of cellular proteins in multiple organisms,” Bioinforma. Oxf. Engl., vol. 33, no. 5, pp. 749–750, Mar. 2017, doi: 10.1093/bioinformatics/btw717

2017 doi
[79]

mLASSO-Hum: A LASSO-based interpretable human-protein subcellular localization predictor,

S. Wan, M.-W. Mak, and S.-Y. Kung, “mLASSO-Hum: A LASSO-based interpretable human-protein subcellular localization predictor,” J. Theor. Biol., vol. 382, pp. 223– 234, Oct. 2015, doi: 10.1016/j.jtbi.2015.06.042

2015 doi
[80]

Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins,

S. Wan, M.-W. Mak, and S.-Y. Kung, “Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins,” BMC Bioinformatics, vol. 17, p. 97, Feb. 2016, doi: 10.1186/s12859-016-0940-x

2016 doi
[81]

Gram-LocEN: Interpretable prediction of subcellular multi-localization of Gram-positive and Gram-negative bacterial proteins,

S. Wan, M.-W. Mak, and S.-Y. Kung, “Gram-LocEN: Interpretable prediction of subcellular multi-localization of Gram-positive and Gram-negative bacterial proteins,” Chemom. Intell. Lab. Syst., vol. 162, pp. 1–9, Mar. 2017, doi: 10.1016/j.chemolab.2016.12.014

2017 doi
[82]

Ensemble Linear Neighborhood Propagation for Predicting Subchloroplast Localization of Multi-Location Proteins,

S. Wan, M.-W. Mak, and S.-Y. Kung, “Ensemble Linear Neighborhood Propagation for Predicting Subchloroplast Localization of Multi-Location Proteins,” J. Proteome Res., vol. 15, no. 12, pp. 4755–4762, Dec. 2016, doi: 10.1021/acs.jproteome.6b00686

2016 doi
[83]

Transductive Learning for Multi-Label Protein Subchloroplast Localization Prediction,

S. Wan, M.-W. Mak, and S.-Y. Kung, “Transductive Learning for Multi-Label Protein Subchloroplast Localization Prediction,” IEEE/ACM Trans. Comput. Biol. Bioinform., vol. 14, no. 1, pp. 212–224, 2017, doi: 10.1109/TCBB.2016.2527657

2017
[84]

Mem-mEN: Predicting Multi-Functional Types of Membrane Proteins by Interpretable Elastic Nets,

S. Wan, M.-W. Mak, and S.-Y. Kung, “Mem-mEN: Predicting Multi-Functional Types of Membrane Proteins by Interpretable Elastic Nets,” IEEE/ACM Trans. Comput. Biol. Bioinform., vol. 13, no. 4, pp. 706–718, 2016, doi: 10.1109/TCBB.2015.2474407

2016
[85]

Mem-ADSVM: A two-layer multi-label predictor for identifying multi- functional types of membrane proteins,

S. Wan, M.-W. Mak, and S.-Y. Kung, “Mem-ADSVM: A two-layer multi-label predictor for identifying multi- functional types of membrane proteins,” J. Theor. Biol., vol. 398, pp. 32–42, Jun. 2016, doi: 10.1016/j.jtbi.2016.03.013

2016 doi
[86]

Prot-SCL: State of the art Prediction of Protein Subcellular Localization from Primary Sequence Using Contrastive Learning,

S. Giannakoulias, J. J. Ferrie, A. Apicello, and C. Mitchell, “Prot-SCL: State of the art Prediction of Protein Subcellular Localization from Primary Sequence Using Contrastive Learning,” Sep. 05, 2023, bioRxiv. doi: 10.1101/2023.09.01.555932

2023 doi
[87]

Identify RNA-associated subcellular localizations based on multi- label learning using Chou’s 5-steps rule,

H. Wang, Y. ing, J. Tang, Q. Zou, and F. Guo, “Identify RNA-associated subcellular localizations based on multi- label learning using Chou’s 5-steps rule,” BMC Genomics, vol. 22, no. 1, p. 56, Jan. 2021, doi: 10.1186/s12864-020-07347-7

2021 doi
[88]

Human proteins characterization with subcellular localizations,

L. Yang, Y. Lv, T. Li, Y. Zuo, and W. Jiang, “Human proteins characterization with subcellular localizations,” J. Theor. Biol., vol. 358, pp. 61–73, Oct. 2014, doi: 10.1016/j.jtbi.2014.05.008

2014 doi
[89]

Multi- label learning for identi_cation of RNA-associated subcellular localizations,

H. Wang, Y. ing, J. Tang, Q. Zou, and F. Guo, “Multi- label learning for identi_cation of RNA-associated subcellular localizations,” Aug. 19, 2020. doi: 10.21203/rs.3.rs-55447/v1

2020 doi
[90]

System-wide analysis of RNA and protein subcellular localization dynamics,

E. Villanueva et al., “System-wide analysis of RNA and protein subcellular localization dynamics,” Nat. Methods, vol. 21, no. 1, pp. 60–71, Jan. 2024, doi: 10.1038/s41592- 023-02101-9

2024 doi
[91]

Predicting protein subcellular location with network embedding and enrichment features,

X. Pan, L. Lu, and Y.- . Cai, “Predicting protein subcellular location with network embedding and enrichment features,” Biochim. Biophys. Acta Proteins Proteomics, vol. 1868, no. 10, p. 140477, Oct. 2020, doi: 10.1016/j.bbapap.2020.140477

2020
[92]

Predicting subcellular location of protein with evolution information and sequence-based deep learning,

Z. Liao, G. Pan, C. Sun, and J. Tang, “Predicting subcellular location of protein with evolution information and sequence-based deep learning,” BMC Bioinformatics, vol. 22, no. 10, p. 515, Oct. 2021, doi: 10.1186/s12859- 021-04404-0

2021 doi
[93]

A Review for Artificial Intelligence Based Protein Subcellular Localization,

H. Xiao, Y. Zou, J. Wang, and S. Wan, “A Review for Artificial Intelligence Based Protein Subcellular Localization,” Biomolecules, vol. 14, no. 4, p. 409, Mar. 2024, doi: 10.3390/biom14040409

2024 doi
[94]

Going from where to why—interpretable prediction of protein subcellular localization,

S. Briesemeister, J. Rahnenführer, and O. Kohlbacher, “Going from where to why—interpretable prediction of protein subcellular localization,” Bioinformatics, vol. 26, no. 9, p. 1232, Mar. 2010, doi: 10.1093/bioinformatics/btq115

2010 doi
[95]

RNALocate v2.0: an updated resource for RNA subcellular localization with increased coverage and annotation,

T. Cui et al., “RNALocate v2.0: an updated resource for RNA subcellular localization with increased coverage and annotation,” Nucleic Acids Res., vol. 50, no. D1, pp. D333–D339, Jan. 2022, doi: 10.1093/nar/gkab825

2022 doi
[96]

I LncLoc: Subcellular Localization of LncRNAs Based on a Framework for Imbalanced Data istributions,

Y. Wang et al., “I LncLoc: Subcellular Localization of LncRNAs Based on a Framework for Imbalanced Data istributions,” Interdiscip. Sci. Comput. Life Sci., vol. 14, no. 2, pp. 409–420, Jun. 2022, doi: 10.1007/s12539-021- 00497-6

2022 doi
[97]

lncSLPre: An Ensemble Method with Multi-Source Sequence Descriptors to Predict lncRNA Subcellular Localizations from Imbalanced ata,

R. Yang, S. Gao, Y. Fu, and L. Zhang, “lncSLPre: An Ensemble Method with Multi-Source Sequence Descriptors to Predict lncRNA Subcellular Localizations from Imbalanced ata,” Jul. 26, 2023, Rochester, NY: 4515036. doi: 10.2139/ssrn.4515036

2023 doi
[98]

Imaging individual mRNA molecules using multiple singly labeled probes,

A. Raj, P. van den Bogaard, S. A. Rifkin, A. van Oudenaarden, and S. Tyagi, “Imaging individual mRNA molecules using multiple singly labeled probes,” Nat. Methods, vol. 5, no. 10, pp. 877–879, Oct. 2008, doi: 10.1038/nmeth.1253

2008 doi
[99]

Spatially resolved, highly multiplexed RNA profiling in single cells,

K. H. Chen, A. N. Boettiger, J. R. Moffitt, S. Wang, and X. Zhuang, “Spatially resolved, highly multiplexed RNA profiling in single cells,” Science, vol. 348, no. 6233, p. aaa6090, Apr. 2015, doi: 10.1126/science.aaa6090

2015 doi
[100]

Transcriptome-scale super- resolved imaging in tissues by RNA seqFISH+,

C.-H. L. Eng et al., “Transcriptome-scale super- resolved imaging in tissues by RNA seqFISH+,” Nature, vol. 568, no. 7751, pp. 235–239, Apr. 2019, doi: 10.1038/s41586-019-1049-y

2019 doi
[101]

Multiplex digital spatial profiling of proteins and RNA in fixed tissue,

C. R. Merritt et al., “Multiplex digital spatial profiling of proteins and RNA in fixed tissue,” Nat. Biotechnol., vol. 38, no. 5, pp. 586–599, May 2020, doi: 10.1038/s41587-020-0472-9

2020 doi
[102]

Live-cell mapping of organelle- associated RNAs via proximity biotinylation combined with protein-RNA crosslinking,

P. Kaewsapsak, D. M. Shechner, W. Mallard, J. L. Rinn, and A. Y. Ting, “Live-cell mapping of organelle- associated RNAs via proximity biotinylation combined with protein-RNA crosslinking,” eLife, vol. 6, p. e29224, Dec. 2017, doi: 10.7554/eLife.29224

2017 doi
[103]

CeFra-seq reveals broad asymmetric mRNA and noncoding RNA distribution profiles in rosophila and human cells,

L. P. Benoit Bouvrette et al., “CeFra-seq reveals broad asymmetric mRNA and noncoding RNA distribution profiles in rosophila and human cells,” RNA N. Y. N, vol. 24, no. 1, pp. 98–113, Jan. 2018, doi: 10.1261/rna.063172.117

2018 doi
[104]

CLIP-Seq to identify targets and interactions of RNA binding proteins and RNA modifying enzymes,

J. Stoute and K. F. Liu, “CLIP-Seq to identify targets and interactions of RNA binding proteins and RNA modifying enzymes,” Methods Enzymol., vol. 658, p. 419, Aug. 2021, doi: 10.1016/bs.mie.2021.08.001

2021 doi
[105]

22, 2024

“MiRGOFS: a GO-based functional similarity measurement for miRNAs, with applications to the prediction of miRNA subcellular localization and miRNA–disease association | Bioinformatics | Oxford Academic.” Accessed: Nov. 22, 2024. [Online]. Available: https://academic.oup.com/bi...

2024
[106]

MiRLoc: predicting miRNA subcellular localization by incorporating miRNA-mRNA interactions and mRNA subcellular localization,

M. Xu, Y. Chen, Z. Xu, L. Zhang, H. Jiang, and C. Pian, “MiRLoc: predicting miRNA subcellular localization by incorporating miRNA-mRNA interactions and mRNA subcellular localization,” Brief. Bioinform., vol. 23, no. 2, p. bbac044, Mar. 2022, doi: 10.1093/bib/bbac044

2022 doi
[107]

miRNALoc: predicting miRNA subcellular localizations based on principal component scores of physico-chemical properties and pseudo compositions of di-nucleotides,

P. K. Meher, S. Satpathy, and A. R. Rao, “miRNALoc: predicting miRNA subcellular localizations based on principal component scores of physico-chemical properties and pseudo compositions of di-nucleotides,” Sci. Rep., vol. 10, no. 1, p. 14557, Sep. 2020, doi: 10.1038/s41598-020-71381-4

2020 doi
[108]

Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences,

W. Li and A. Godzik, “Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences,” Bioinformatics, vol. 22, no. 13, pp. 1658–1659, Jul. 2006, doi: 10.1093/bioinformatics/btl158

2006 doi
[109]

SMOTE: Synthetic Minority Over- sampling Technique,

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over- sampling Technique,” Jun. 09, 2011, arXiv: arXiv:1106.1813. doi: 10.48550/arXiv.1106.1813

2011 doi arXiv
[110]

A survey of k-mer methods and applications in bioinformatics,

C. Moeckel et al., “A survey of k-mer methods and applications in bioinformatics,” Comput. Struct. Biotechnol. J., vol. 23, pp. 2289–2303, Dec. 2024, doi: 10.1016/j.csbj.2024.05.025

2024 doi
[111]

RNAlight: a machine learning model to identify nucleotide features determining RNA subcellular localization,

G.-H. Yuan, Y. Wang, G.-Z. Wang, and L. Yang, “RNAlight: a machine learning model to identify nucleotide features determining RNA subcellular localization,” Brief. Bioinform., vol. 24, no. 1, p. bbac509, Jan. 2023, doi: 10.1093/bib/bbac509

2023 doi
[112]

Prediction of mRNA subcellular localization using deep recurrent neural networks,

Z. Yan, E. Lécuyer, and M. Blanchette, “Prediction of mRNA subcellular localization using deep recurrent neural networks,” Bioinforma. Oxf. Engl., vol. 35, no. 14, pp. i333–i342, Jul. 2019, doi: 10.1093/bioinformatics/btz337

2019 doi
[114]

mRNALoc: a novel machine-learning based in-silico tool to predict mRNA subcellular localization,

A. Garg, N. Singhal, R. Kumar, and M. Kumar, “mRNALoc: a novel machine-learning based in-silico tool to predict mRNA subcellular localization,” Nucleic Acids Res., vol. 48, no. W1, pp. W239–W243, Jul. 2020, doi: 10.1093/nar/gkaa385

2020 doi
[115]

iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC,

Z.-D. Su et al., “iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC,” Bioinformatics, vol. 34, no. 24, pp. 4196–4204, Dec. 2018, doi: 10.1093/bioinformatics/bty508

2018 doi
[116]

Unified mRNA Subcellular Localization Predictor based on machine learning techniques,

S. Musleh, M. Arif, N. M. Alajez, and T. Alam, “Unified mRNA Subcellular Localization Predictor based on machine learning techniques,” BMC Genomics, vol. 25, no. 1, p. 151, Feb. 2024, doi: 10.1186/s12864-024- 10077-9

2024 doi
[117]

Towards a better prediction of subcellular location of long non-coding RNA,

Z.-Y. Zhang, Z.-J. Sun, Y.-H. Yang, and H. Lin, “Towards a better prediction of subcellular location of long non-coding RNA,” Front. Comput. Sci., vol. 16, no. 5, p. 165903, Jan. 2022, doi: 10.1007/s11704-021-1015- 3

2022 doi
[118]

Composition, physicochemical property and base periodicity for discriminating lncRNA and mRNA,

P. Rajesh and A. Krishnamachari, “Composition, physicochemical property and base periodicity for discriminating lncRNA and mRNA,” Bioinformation, vol. 19, no. 12, pp. 1145–1152, 2023, doi: 10.6026/973206300191145

2023 doi
[119]

Common Features in lncRNA Annotation and Classification: A Survey,

C. Klapproth, R. Sen, P. F. Stadler, S. Findeiß, and J. Fallmann, “Common Features in lncRNA Annotation and Classification: A Survey,” Non-Coding RNA, vol. 7, no. 4, p. 77, Dec. 2021, doi: 10.3390/ncrna7040077

2021 doi
[120]

Codon and amino acid content are associated with mRNA stability in mammalian cells,

M. E. Forrest, O. Pinkard, S. Martin, T. J. Sweet, G. Hanson, and J. Coller, “Codon and amino acid content are associated with mRNA stability in mammalian cells,” PLOS ONE, vol. 15, no. 2, p. e0228730, Feb. 2020, doi: 10.1371/journal.pone.0228730

2020 doi
[121]

Three- Dimensional Mapping of mRNA Export through the Nuclear Pore Complex,

S. J. Schnell, J. Ma, and W. Yang, “Three- Dimensional Mapping of mRNA Export through the Nuclear Pore Complex,” Genes, vol. 5, no. 4, pp. 1032– 1049, Nov. 2014, doi: 10.3390/genes5041032

2014 doi
[122]

3 genomic regulation of lncRNA and Xist in X chromosome,

F. Yan, X. Wang, and Y. Zeng, “3 genomic regulation of lncRNA and Xist in X chromosome,” Semin. Cell Dev. Biol., vol. 90, pp. 174–180, Jun. 2019, doi: 10.1016/j.semcdb.2018.07.013

2019 doi
[123]

Large language models and their applications in bioinformatics,

O. A. Sarumi and . Heider, “Large language models and their applications in bioinformatics,” Comput. Struct. Biotechnol. J., vol. 23, pp. 3498–3505, Dec. 2024, doi: 10.1016/j.csbj.2024.09.031

2024 doi
[124]

Locate-R: Subcellular localization of long non-coding RNAs using nucleotide compositions,

A. Ahmad, H. Lin, and S. Shatabda, “Locate-R: Subcellular localization of long non-coding RNAs using nucleotide compositions,” Genomics, vol. 112, no. 3, pp. 2583–2589, May 2020, doi: 10.1016/j.ygeno.2020.02.011

2020 doi
[125]

mRNALocater: Enhance the prediction accuracy of eukaryotic mRNA subcellular localization by using model fusion strategy,

Q. Tang, F. Nie, J. Kang, and W. Chen, “mRNALocater: Enhance the prediction accuracy of eukaryotic mRNA subcellular localization by using model fusion strategy,” Mol. Ther. J. Am. Soc. Gene Ther., vol. 29, no. 8, pp. 2617–2623, Aug. 2021, doi: 10.1016/j.ymthe.2021.04.004

2021 doi
[126]

SubLocEP: a novel ensemble predictor of subcellular localization of eukaryotic mRNA based on machine learning,

J. Li, L. Zhang, S. He, F. Guo, and Q. Zou, “SubLocEP: a novel ensemble predictor of subcellular localization of eukaryotic mRNA based on machine learning,” Brief. Bioinform., vol. 22, no. 5, p. bbaa401, Sep. 2021, doi: 10.1093/bib/bbaa401

2021 doi
[127]

MSLP: mRNA subcellular localization predictor based on machine learning techniques,

S. Musleh, M. T. Islam, R. Qureshi, N. M. Alajez, and T. Alam, “MSLP: mRNA subcellular localization predictor based on machine learning techniques,” BMC Bioinformatics, vol. 24, no. 1, p. 109, Mar. 2023, doi: 10.1186/s12859-023-05232-0

2023 doi
[128]

LncLocation: Efficient Subcellular Location Prediction of Long Non-Coding RNA-Based Multi-Source Heterogeneous Feature Fusion,

S. Feng, Y. Liang, W. Du, W. Lv, and Y. Li, “LncLocation: Efficient Subcellular Location Prediction of Long Non-Coding RNA-Based Multi-Source Heterogeneous Feature Fusion,” Int. J. Mol. Sci., vol. 21, no. 19, p. 7271, Oct. 2020, doi: 10.3390/ijms21197271

2020 doi
[129]

Lnclocator-imb: An Imbalance-tolerant Ensemble Deep Learning Framework for Predicting Long Non-coding RNA Subcellular Localization,

H. Liu, . Li, and H. Wu, “Lnclocator-imb: An Imbalance-tolerant Ensemble Deep Learning Framework for Predicting Long Non-coding RNA Subcellular Localization,” IEEE J. Biomed. Health Inform., vol. PP, Oct. 2023, doi: 10.1109/JBHI.2023.3324709

2023
[130]

eepLocRNA: an interpretable deep learning model for predicting RNA subcellular localization with domain- specific transfer-learning,

J. Wang, M. Horlacher, L. Cheng, and O. Winther, “ eepLocRNA: an interpretable deep learning model for predicting RNA subcellular localization with domain- specific transfer-learning,” Bioinformatics, vol. 40, no. 2, p. btae065, Feb. 2024, doi: 10.1093/bioinformatics/btae065

2024 doi
[131]

M3Loc: multi-label mRNA subcellular localization prediction and analysis based on multi-head self-attention mechanism,

D. Wang et al., “ M3Loc: multi-label mRNA subcellular localization prediction and analysis based on multi-head self-attention mechanism,” Nucleic Acids Res., vol. 49, no. 8, p. e46, May 2021, doi: 10.1093/nar/gkab016

2021 doi
[132]

MRSLpred-a hybrid approach for predicting multi-label subcellular localization of mRNA at the genome scale,

S. Choudhury, N. Bajiya, S. Patiyal, and G. P. S. Raghava, “MRSLpred-a hybrid approach for predicting multi-label subcellular localization of mRNA at the genome scale,” Front. Bioinforma., vol. 4, p. 1341479, 2024, doi: 10.3389/fbinf.2024.1341479

2024
[133]

MirLocPredictor: A ConvNet- Based Multi-Label MicroRNA Subcellular Localization Predictor by Incorporating k-Mer Positional Information,

M. N. Asim, M. I. Malik, C. Zehe, J. Trygg, A. engel, and S. Ahmed, “MirLocPredictor: A ConvNet- Based Multi-Label MicroRNA Subcellular Localization Predictor by Incorporating k-Mer Positional Information,” Genes, vol. 11, no. 12, p. 1475, Dec. 2020, doi: 10.3390/genes11121475

2020 doi
[134]

MMLmiRLocNet: miRNA Subcellular Localization Prediction based on Multi-view Multi-label Learning for rug esign,

T. Bai, J. Xie, Y. Liu, and B. Liu, “MMLmiRLocNet: miRNA Subcellular Localization Prediction based on Multi-view Multi-label Learning for rug esign,” IEEE J. Biomed. Health Inform., vol. PP, Oct. 2024, doi: 10.1109/JBHI.2024.3483997

2024
[135]

K -KLNMF: Identification of lncRNAs subcellular localization with multiple features and nonnegative matrix factorization,

S. Zhang and H. Qiao, “K -KLNMF: Identification of lncRNAs subcellular localization with multiple features and nonnegative matrix factorization,” Anal. Biochem., vol. 610, p. 113995, Dec. 2020, doi: 10.1016/j.ab.2020.113995

2020
[137]

Predictive models of subcellular localization of long RNAs,

B. Zuckerman and I. Ulitsky, “Predictive models of subcellular localization of long RNAs,” RNA N. Y. N, vol. 25, no. 5, pp. 557–572, May 2019, doi: 10.1261/rna.068288.118

2019 doi
[138]

A BERT-based model for the prediction of lncRNA subcellular localization in Homo sapiens,

Z.-Y. Zhang, Z. Zhang, X. Ye, T. Sakurai, and H. Lin, “A BERT-based model for the prediction of lncRNA subcellular localization in Homo sapiens,” Int. J. Biol. Macromol., vol. 265, no. Pt 1, p. 130659, Apr. 2024, doi: 10.1016/j.ijbiomac.2024.130659

2024
[139]

PreSubLncR: Predicting Subcellular Localization of Long Non-Coding RNA Based on Multi-Scale Attention Convolutional Network and Bidirectional Long Short- Term Memory Network,

X. Wang, S. Wang, R. Wang, and X. Gao, “PreSubLncR: Predicting Subcellular Localization of Long Non-Coding RNA Based on Multi-Scale Attention Convolutional Network and Bidirectional Long Short- Term Memory Network,” Processes, vol. 12, no. 4, Art. no. 4, Apr. 2024, doi: 10.339...

2024 doi
[140]

GATLncLoc+C&S: Prediction of LncRNA subcellular localization based on corrective graph attention network,

X. Deng, L. Tang, and L. Liu, “GATLncLoc+C&S: Prediction of LncRNA subcellular localization based on corrective graph attention network,” Mar. 12, 2024, bioRxiv. doi: 10.1101/2024.03.08.584063

2024 doi
[141]

Application of Graph Attention Networks in LncRNA Subcellular Localization Prediction,

<p>Xi eng</p>, “Application of Graph Attention Networks in LncRNA Subcellular Localization Prediction,” Acad. J. Comput. Inf. Sci., vol. 7, no. 3, Mar. 2024, doi: 10.25236/AJCIS.2024.070310

2024
[143]

NN-RNALoc: Neural network-based model for prediction of mRNA sub-cellular localization using distance-based sub-sequence profiles,

N. S. Babaiha, R. Aghdam, S. Ghiam, and C. Eslahchi, “NN-RNALoc: Neural network-based model for prediction of mRNA sub-cellular localization using distance-based sub-sequence profiles,” PloS One, vol. 18, no. 9, p. e0258793, 2023, doi: 10.1371/journal.pone.0258793

2023 doi
[144]

GraphLncLoc: long non-coding RNA subcellular localization prediction using graph convolutional networks based on sequence to graph transformation,

M. Li, B. Zhao, R. Yin, C. Lu, F. Guo, and M. Zeng, “GraphLncLoc: long non-coding RNA subcellular localization prediction using graph convolutional networks based on sequence to graph transformation,” Brief. Bioinform., vol. 24, no. 1, p. bbac565, Jan. 2023, doi: 10.1093/bib/bbac565

2023 doi
[145]

GM- lncLoc: LncRNAs subcellular localization prediction based on graph neural network with meta-learning,

J. Cai, T. Wang, X. eng, L. Tang, and L. Liu, “GM- lncLoc: LncRNAs subcellular localization prediction based on graph neural network with meta-learning,” BMC Genomics, vol. 24, no. 1, p. 52, Jan. 2023, doi: 10.1186/s12864-022-09034-1

2023 doi
[146]

LightGBM- LncLoc: A LightGBM-Based Computational Predictor for Recognizing Long Non-Coding RNA Subcellular Localization,

J. Lyu, P. Zheng, Y. Qi, and G. Huang, “LightGBM- LncLoc: A LightGBM-Based Computational Predictor for Recognizing Long Non-Coding RNA Subcellular Localization,” Mathematics, vol. 11, no. 3, Art. no. 3, Jan. 2023, doi: 10.3390/math11030602

2023 doi
[148]

lncRNALoc: A discrete wavelet transform-based model for predicting lncRNA subcellular localization,

X. Fu, Y. Chen, and S. Tian, “ lncRNALoc: A discrete wavelet transform-based model for predicting lncRNA subcellular localization,” Math. Biosci. Eng. MBE, vol. 20, no. 12, pp. 20648–20667, Nov. 2023, doi: 10.3934/mbe.2023913

2023 doi
[149]

SGCL-LncLoc: An Interpretable Deep Learning Model for Improving IncRNA Subcellular Localization Prediction with Supervised Graph Contrastive Learning,

M. Li et al., “SGCL-LncLoc: An Interpretable Deep Learning Model for Improving IncRNA Subcellular Localization Prediction with Supervised Graph Contrastive Learning,” Big Data Min. Anal., vol. 7, no. 3, pp. 765–780, Sep. 2024, doi: 10.26599/BDMA.2024.9020002

2024
[150]

Lnc LSM: Identification of Long Non-coding RNAs with Deep Learning-based Sequence Model,

Y. Wang, P. Zhao, H. Du, Y. Cao, Q. Peng, and L. Fu, “Lnc LSM: Identification of Long Non-coding RNAs with Deep Learning-based Sequence Model,” IEEE J. Biomed. Health Inform., vol. PP, Feb. 2023, doi: 10.1109/JBHI.2023.3247805

2023
[152]

Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations,

Y. Bi et al., “Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations,” Brief. Bioinform., vol. 23, no. 6, p. bbac467, Nov. 2022, doi: 10.1093/bib/bbac467

2022 doi
[153]

eepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding,

M. Zeng, Y. Wu, C. Lu, F. Zhang, F.-X. Wu, and M. Li, “ eepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding,” Brief. Bioinform., vol. 23, no. 1, p. bbab360, Jan. 2022, doi: 10.1093/bib/bbab360

2022 doi
[154]

TACOS: a novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization,

Y.-J. Jeon, M. M. Hasan, H. W. Park, K. W. Lee, and B. Manavalan, “TACOS: a novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization,” Brief. Bioinform., vol. 23, no. 4, p. bbac243, Jul. 2022, doi: 10.1093/bib/bbac243

2022 doi
[155]

mLoc-mRNA: predicting multiple sub-cellular localization of mRNAs using random forest algorithm coupled with feature selection via elastic net,

P. K. Meher, A. Rai, and A. R. Rao, “mLoc-mRNA: predicting multiple sub-cellular localization of mRNAs using random forest algorithm coupled with feature selection via elastic net,” BMC Bioinformatics, vol. 22, p. 342, Jun. 2021, doi: 10.1186/s12859-021-04264-8

2021 doi
[156]

A eep Learning Approach to LncRNA Subcellular Localization Using Inexact q-mers,

W. Yi and . A. Adjeroh, “A eep Learning Approach to LncRNA Subcellular Localization Using Inexact q-mers,” in 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Dec. 2021, pp. 2128–2133. doi: 10.1109/BIBM52615.2021.9669409

2021
[157]

lncLocPred: Predicting LncRNA Subcellular Localization Using Multiple Sequence Feature Information,

Y. Fan, M. Chen, and Q. Zhu, “lncLocPred: Predicting LncRNA Subcellular Localization Using Multiple Sequence Feature Information,” IEEE Access, vol. 8, pp. 124702–124711, 2020, doi: 10.1109/ACCESS.2020.3007317

2020
[158]

Prediction of LncRNA Subcellular Localization with Deep Learning from Sequence Features,

B. L. Gudenas and L. Wang, “Prediction of LncRNA Subcellular Localization with Deep Learning from Sequence Features,” Sci. Rep., vol. 8, no. 1, p. 16385, Nov. 2018, doi: 10.1038/s41598-018-34708-w

2018 doi
[159]

The lncLocator: a subcellular localization predictor for long non-coding RNAs based on a stacked ensemble classifier,

Z. Cao, X. Pan, Y. Yang, Y. Huang, and H.-B. Shen, “The lncLocator: a subcellular localization predictor for long non-coding RNAs based on a stacked ensemble classifier,” Bioinforma. Oxf. Engl., vol. 34, no. 13, pp. 2185–2194, Jul. 2018, doi: 10.1093/bioinformatics/bty085

2018 doi
[160]

eepLNC, a long non-coding RNA prediction tool using deep neural network,

R. Tripathi, S. Patel, V. Kumari, P. Chakraborty, and P. K. Varadwaj, “ eepLNC, a long non-coding RNA prediction tool using deep neural network,” Netw. Model. Anal. Health Inform. Bioinforma., vol. 5, no. 1, p. 21, Jun. 2016, doi: 10.1007/s13721-016-0129-2

2016 doi
[161]

PairProSVM: Protein Subcellular Localization Based on Local Pairwise Profile Alignment and SVM,

M.-W. Mak, J. Guo, and S.-Y. Kung, “PairProSVM: Protein Subcellular Localization Based on Local Pairwise Profile Alignment and SVM,” IEEE/ACM Trans. Comput. Biol. Bioinform., vol. 5, no. 3, pp. 416–422, Jul. 2008, doi: 10.1109/TCBB.2007.70256

2008
[162]

An effective machine learning-based model for the prediction of protein–protein interaction sites in health systems,

M. Tahir, F. Khan, M. Hayat, and M. D. Alshehri, “An effective machine learning-based model for the prediction of protein–protein interaction sites in health systems,” Neural Comput. Appl., vol. 36, no. 1, pp. 65– 75, Jan. 2024, doi: 10.1007/s00521-022-07024-8

2024 doi
[163]

eep Forest-based Prediction of Protein Subcellular Localization,

L. Zhao, J. Wang, M. M. Nabil, and J. Zhang, “ eep Forest-based Prediction of Protein Subcellular Localization,” Curr. Gene Ther., vol. 18, no. 5, pp. 268– 274, 2018, doi: 10.2174/1566523218666180913110949

2018 doi
[164]

Accurate Identification of Submitochondrial Protein Location Based on eep Representation Learning Feature Fusion,

J. Sui, Y. Chen, Y. Cao, and Y. Zhao, “Accurate Identification of Submitochondrial Protein Location Based on eep Representation Learning Feature Fusion,” in Advanced Intelligent Computing Technology and Applications, D.-S. Huang, P. Premaratne, B. Jin, B. Qu, K.-H. Jo, and A. ...

2023 doi
[165]

L2S-MirLoc: A Lightweight Two Stage MiRNA Sub-Cellular Localization Prediction Framework,

M. N. Asim et al., “L2S-MirLoc: A Lightweight Two Stage MiRNA Sub-Cellular Localization Prediction Framework,” in 2021 International Joint Conference on Neural Networks (IJCNN), Jul. 2021, pp. 1–8. doi: 10.1109/IJCNN52387.2021.9534015

2021
[166]

Identify ncRNA Subcellular Localization via Graph Regularized k-Local Hyperplane Distance Nearest Neighbor Model on Multi-Kernel Learning,

H. Zhou, H. Wang, J. Tang, Y. Ding, and F. Guo, “Identify ncRNA Subcellular Localization via Graph Regularized k-Local Hyperplane Distance Nearest Neighbor Model on Multi-Kernel Learning,” IEEE/ACM Trans. Comput. Biol. Bioinform., vol. 19, no. 6, pp. 3517– 3529, 2022, doi: 10....

2022
[167]

Shared subspace-based radial basis function neural network for identifying ncRNAs subcellular localization,

Y. ing, P. Tiwari, F. Guo, and Q. Zou, “Shared subspace-based radial basis function neural network for identifying ncRNAs subcellular localization,” Neural Netw., vol. 156, pp. 170–178, Dec. 2022, doi: 10.1016/j.neunet.2022.09.026

2022 doi
[168]

Predicting phenotypes from microarrays using amplified, initially marginal, eigenvector regression,

L. ing and . J. Mc onald, “Predicting phenotypes from microarrays using amplified, initially marginal, eigenvector regression,” Bioinforma. Oxf. Engl., vol. 33, no. 14, pp. i350–i358, Jul. 2017, doi: 10.1093/bioinformatics/btx265

2017 doi
[169]

GP-HTNLoc: A graph prototype head-tail network-based model for multi-label subcellular localization prediction of ncRNAs,

S. Han and L. Liu, “GP-HTNLoc: A graph prototype head-tail network-based model for multi-label subcellular localization prediction of ncRNAs,” Comput. Struct. Biotechnol. J., vol. 23, pp. 2034–2048, Dec. 2024, doi: 10.1016/j.csbj.2024.04.052

2024 doi
[170]

ncRNALocate-EL: a multi-label ncRNA subcellular locality prediction model based on ensemble learning,

T. Bai and B. Liu, “ncRNALocate-EL: a multi-label ncRNA subcellular locality prediction model based on ensemble learning,” Brief. Funct. Genomics, vol. 22, no. 5, pp. 442–452, Nov. 2023, doi: 10.1093/bfgp/elad007

2023 doi
[171]

Long Short-Term Memory | MIT Press Journals & Magazine | IEEE Xplore

“Long Short-Term Memory | MIT Press Journals & Magazine | IEEE Xplore.” Accessed: ec. 16, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/6795963

2024
[172]

lncLocator 2.0: a cell-line-specific subcellular localization predictor for long non-coding RNAs with interpretable deep learning | Bioinformatics | Oxford Academic

“lncLocator 2.0: a cell-line-specific subcellular localization predictor for long non-coding RNAs with interpretable deep learning | Bioinformatics | Oxford Academic.” Accessed: Nov. 22, 2024. [Online]. Available: https://academic.oup.com/bioinformatics/article/37/16/23 08/6149475

2024
[173]

iLoc-miRNA: extracellular/intracellular miRNA prediction using deep BiLSTM with attention mechanism | Briefings in Bioinformatics | Oxford Academic

“iLoc-miRNA: extracellular/intracellular miRNA prediction using deep BiLSTM with attention mechanism | Briefings in Bioinformatics | Oxford Academic.” Accessed: Nov. 22, 2024. [Online]. Available: https://academic.oup.com/bib/article/23/5/bbac395/66936 01

2024
[174]

PMiSLocMF: predicting miRNA subcellular localizations by incorporating multi-source features of miRNAs,

L. Chen, J. Gu, and B. Zhou, “PMiSLocMF: predicting miRNA subcellular localizations by incorporating multi-source features of miRNAs,” Brief. Bioinform., vol. 25, no. 5, p. bbae386, Sep. 2024, doi: 10.1093/bib/bbae386

2024 doi
[175]

A Unified Approach to Interpreting Model Predictions,

S. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” Nov. 25, 2017, arXiv: arXiv:1705.07874. doi: 10.48550/arXiv.1705.07874

2017 doi arXiv
[176]

MGFmiRNAloc: Predicting miRNA Subcellular Localization Using Molecular Graph Feature and Convolutional Block Attention Module | IEEE Journals & Magazine | IEEE Xplore

“MGFmiRNAloc: Predicting miRNA Subcellular Localization Using Molecular Graph Feature and Convolutional Block Attention Module | IEEE Journals & Magazine | IEEE Xplore.” Accessed: Nov. 22, 2024. [Online]. Available: https://ieeexplore.ieee.org/document/10487110

2024
[177]

AmiRLocGNet: miRNA subcellular localization prediction by combining miRNA–disease associations and graph convolutional networks | Briefings in Bioinformatics | Oxford Academic

“ AmiRLocGNet: miRNA subcellular localization prediction by combining miRNA–disease associations and graph convolutional networks | Briefings in Bioinformatics | Oxford Academic.” Accessed: Nov. 22,
[181]

A eep Learning Approach To Identify MRNA Localization Patterns,

R. Dubois et al., “A eep Learning Approach To Identify MRNA Localization Patterns,” in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Apr. 2019, pp. 1386–1390. doi: 10.1109/ISBI.2019.8759235

2019
[182]

A computational framework to study sub-cellular RNA localization,

A. Samacoits et al., “A computational framework to study sub-cellular RNA localization,” Nat. Commun., vol. 9, no. 1, p. 4584, Nov. 2018, doi: 10.1038/s41467-018- 06868-w

2018 doi
[183]

Automated analysis of high- content microscopy data with deep learning,

O. Z. Kraus et al., “Automated analysis of high- content microscopy data with deep learning,” Mol. Syst. Biol., vol. 13, no. 4, p. 924, Apr. 2017, doi: 10.15252/msb.20177551

2017 doi
[184]

Faster fluorescence microscopy: advances in high speed biological imaging,

P. W. Winter and H. Shroff, “Faster fluorescence microscopy: advances in high speed biological imaging,” Curr. Opin. Chem. Biol., vol. 20, pp. 46–53, Jun. 2014, doi: 10.1016/j.cbpa.2014.04.008

2014 doi
[185]

FISH-quant v2: a scalable and modular tool for smFISH image analysis,

A. Imbert et al., “FISH-quant v2: a scalable and modular tool for smFISH image analysis,” RNA N. Y. N, vol. 28, no. 6, pp. 786–795, Jun. 2022, doi: 10.1261/rna.079073.121

2022 doi
[186]

FISHFactor: a probabilistic factor model for spatial transcriptomics data with subcellular resolution,

F. C. Walter, O. Stegle, and B. Velten, “FISHFactor: a probabilistic factor model for spatial transcriptomics data with subcellular resolution,” Bioinformatics, vol. 39, no. 5, p. btad183, May 2023, doi: 10.1093/bioinformatics/btad183

2023 doi
[187]

A subcellular map of the human proteome,

P. J. Thul et al., “A subcellular map of the human proteome,” Science, vol. 356, no. 6340, p. eaal3321, May 2017, doi: 10.1126/science.aal3321

2017 doi
[189]

iscovery of molecular features underlying the morphological landscape by integrating spatial transcriptomic data with deep features of tissue images,

S. Bae, H. Choi, and . S. Lee, “ iscovery of molecular features underlying the morphological landscape by integrating spatial transcriptomic data with deep features of tissue images,” Nucleic Acids Res., vol. 49, no. 10, p. e55, Jun. 2021, doi: 10.1093/nar/gkab095

2021 doi
[190]

Comparison of Regularization Methods for ImageNet Classification with Deep Convolutional Neural Networks,

E. A. Smirnov, D. M. Timoshenko, and S. N. Andrianov, “Comparison of Regularization Methods for ImageNet Classification with Deep Convolutional Neural Networks,” AASRI Procedia, vol. 6, pp. 89–94, Jan. 2014, doi: 10.1016/j.aasri.2014.05.013

2014 doi
[191]

Multimodal deep learning model on interim [18F]FDG PET/CT for predicting primary treatment failure in diffuse large B-cell lymphoma,

C. Yuan et al., “Multimodal deep learning model on interim [18F]FDG PET/CT for predicting primary treatment failure in diffuse large B-cell lymphoma,” Eur. Radiol., vol. 33, no. 1, pp. 77–88, Jan. 2023, doi: 10.1007/s00330-022-09031-8

2023 doi
[192]

GPT-4o System Card,

OpenAI et al., “GPT-4o System Card,” Oct. 25, 2024, arXiv: arXiv:2410.21276. doi: 10.48550/arXiv.2410.21276

2024 doi arXiv
[193]

eep Multi-modal Fusion of Image and Non-image Data in Disease Diagnosis and Prognosis: A Review,

C. Cui et al., “ eep Multi-modal Fusion of Image and Non-image Data in Disease Diagnosis and Prognosis: A Review,” Jan. 26, 2023, arXiv: arXiv:2203.15588. doi: 10.48550/arXiv.2203.15588

2023 doi arXiv
[195]

A review of machine learning- based prediction of lncRNA subcellular localization,

X. eng and L. Liu, “A review of machine learning- based prediction of lncRNA subcellular localization,” Adv. Comput. Signals Syst., vol. 7, no. 9, pp. 58–63, Nov. 2023, doi: 10.23977/acss.2023.070908

2023
[2024]

Available: https://academic.oup.com/bib/article/24/4/bbad212/71999 01

[Online]. Available: https://academic.oup.com/bib/article/24/4/bbad212/71999 01

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [14]

Localization of actin messenger RNA during early ascidian development,

W. R. Jeffery, C. R. Tomlinson, and R. D. Brodeur, “Localization of actin messenger RNA during early ascidian development,” Dev. Biol., vol. 99, no. 2, pp. 408–417, Oct. 1983, doi: 10.1016/0012-1606(83)90290- 7

work page doi:10.1016/0012-1606(83)90290- 1983

[2] [188]

On the use of Ripley’s K-function and its derivatives to analyze domain size,

M. A. Kiskowski, J. F. Hancock, and A. K. Kenworthy, “On the use of Ripley’s K-function and its derivatives to analyze domain size,” Biophys. J., vol. 97, no. 4, pp. 1095–1103, Aug. 2009, doi: 10.1016/j.bpj.2009.05.039

work page doi:10.1016/j.bpj.2009.05.039 2009

[3] [194]

SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer,

Z. Huang et al., “SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer,” Front. Genet., vol. 10, Mar. 2019, doi: 10.3389/fgene.2019.00166

arXiv 2019

[4] [179]

Bento: a toolkit for subcellular analysis of spatial transcriptomics data,

C. K. Mah et al., “Bento: a toolkit for subcellular analysis of spatial transcriptomics data,” Genome Biol., vol. 25, no. 1, p. 82, Apr. 2024, doi: 10.1186/s13059-024- 03217-7

work page doi:10.1186/s13059-024- 2024

[5] [178]

RNA trafficking and subcellular localization-a review of mechanisms, experimental and predictive methodologies,

J. Wang, M. Horlacher, L. Cheng, and O. Winther, “RNA trafficking and subcellular localization-a review of mechanisms, experimental and predictive methodologies,” Brief. Bioinform., vol. 24, no. 5, p. bbad249, Sep. 2023, doi: 10.1093/bib/bbad249

work page doi:10.1093/bib/bbad249 2023

[6] [180]

Prediction of RNA subcellular localization: Learning from heterogeneous data sources,

A. F. Savulescu, E. Bouilhol, N. Beaume, and M. Nikolski, “Prediction of RNA subcellular localization: Learning from heterogeneous data sources,” iScience, vol. 24, no. 11, p. 103298, Nov. 2021, doi: 10.1016/j.isci.2021.103298

arXiv 2021

[7] [113]

i NA-Prot|dis: Identifying DNA- Binding Proteins by Incorporating Amino Acid Distance- Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition,

B. Liu et al., “i NA-Prot|dis: Identifying DNA- Binding Proteins by Incorporating Amino Acid Distance- Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition,” PLOS ONE, vol. 9, no. 9, p. e106691, Sep. 2014, doi: 10.1371/journal.pone.0106691

work page doi:10.1371/journal.pone.0106691 2014

[8] [136]

MulStack: An ensemble learning prediction model of multilabel mRNA subcellular localization,

Z. Liu, T. Bai, B. Liu, and L. Yu, “MulStack: An ensemble learning prediction model of multilabel mRNA subcellular localization,” Comput. Biol. Med., vol. 175, p. 108289, Jun. 2024, doi: 10.1016/j.compbiomed.2024.108289

arXiv 2024

[9] [142]

Allocator is a graph neural network-based framework for mRNA subcellular localization prediction,

F. Li, Y. Bi, X. Guo, X. Tan, C. Wang, and S. Pan, “Allocator is a graph neural network-based framework for mRNA subcellular localization prediction,” ec. 15, 2023, bioRxiv. doi: 10.1101/2023.12.14.571762

work page doi:10.1101/2023.12.14.571762 2023

[10] [147]

LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism,

M. Zeng et al., “LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism,” Bioinformatics, vol. 39, no. 12, p. btad752, Dec. 2023, doi: 10.1093/bioinformatics/btad752

work page doi:10.1093/bioinformatics/btad752 2023

[11] [151]

EL-RMLocNet: An explainable LSTM network for RNA-associated multi-compartment localization prediction,

M. N. Asim et al., “EL-RMLocNet: An explainable LSTM network for RNA-associated multi-compartment localization prediction,” Comput. Struct. Biotechnol. J., vol. 20, pp. 3986–4002, 2022, doi: 10.1016/j.csbj.2022.07.031

work page doi:10.1016/j.csbj.2022.07.031 2022