Pith. sign in

REVIEW 4 major objections 3 minor 68 references

ProteoKnight: Convolution-based phage virion protein classification and uncertainty analysis

T0 review · 4 major / 3 minor · reviewed 2026-08-05 · deepseek-v4-flash

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

Pith's one-line read A DNA-walk image encoding lets pretrained convolutional networks classify phage virion proteins with 90.8% binary accuracy and supplies Monte Carlo Dropout uncertainty estimates.

desk verdict A modest, honest applied-ML paper whose readable abstract reports 90.8% binary accuracy on a phage protein task, but with a corrupted full text that prevents auditing the load-bearing encoding design. read the letter →

arxiv 2508.07345 v1 pith:PDPLKTMJ submitted 2025-08-10 cs.LG cs.AI

Samiha Afaf Neha , Abir Ahammed Bhuiyan , Md. Ishrak Khan This is my paper · ORCID
classification cs.LGcs.AI
keywords phagevirionproteinsequenceencodingDNAwalkconvolutionalneuralnetworkstransferlearningMonteCarloDropoutuncertaintyestimationimage-basedclassification
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

The paper tries to establish that protein sequences can be turned into images by adapting the classical DNA-walk algorithm, with each amino acid assigned a pixel color and a walk step, and that pretrained convolutional neural networks can classify phage virion proteins from those images. It reports 90.8% binary classification accuracy, comparable to state-of-the-art methods, and argues this image encoding beats frequency chaos game representation because it preserves spatial and ordering information that frequency counts lose. The paper also claims that Monte Carlo Dropout variance and entropy scores identify low-confidence predictions, with confidence varying by protein class and sequence length. A sympathetic reader would care because this offers a fast, sequence-only route to phage protein annotation with a usable uncertainty flag.

What carries the argument

The load-bearing object is the protein DNA-walk image: each of the 20 amino acids is assigned a pixel color and a step distance/direction, and walking through the sequence paints a trajectory whose shape encodes both residue identity and order. That image is what the pretrained CNN classifies. The comparison method, frequency chaos game representation (FCGR), captures k-mer frequencies but discards the walk's spatial arrangement; ProteoKnight's claimed advantage is retaining that arrangement. Monte Carlo Dropout, applied at inference time, provides the variance and entropy used as uncertainty flags.

What would settle it

Re-run the benchmark after randomly permuting the amino-acid-to-color assignments (keeping step lengths fixed) under the same train/test protocol. If the 90.8% binary accuracy survives the permutation, the specific encoding rules are not the mechanism; if it collapses, the mapping is load-bearing. A second check: hold out a set of recently discovered phage virion proteins not in the training distribution and compare accuracy to the reported benchmark.

Watch

Extended reading notes

Core claim

The paper's central claim is that ProteoKnight, an image-based encoding derived from the DNA-walk algorithm, converts a protein sequence into a colored walk whose geometry retains residue identity and residue order, and that feeding these images to pretrained CNNs yields accurate, robust phage virion protein classification. The authors report 90.8% binary accuracy, show that their encoding surpasses frequency chaos game representation by mitigating spatial information loss, and demonstrate that Monte Carlo Dropout provides informative variance and entropy measures of prediction confidence. Multi-class classification accuracy remains suboptimal, so the claimed strength is specifically the bin

Load-bearing premise

The load-bearing premise is that the hand-chosen mapping from amino acids to pixel colors and walk step sizes preserves enough sequence and positional information for pretrained CNNs to learn PVP-relevant features, rather than merely fitting the benchmark.

Editorial extensions

If this is right

  • If the encoding works as claimed, phage virion proteins can be flagged from sequence alone at roughly 91% binary accuracy, making the method a practical pre-screen for genome annotation pipelines.
  • Because the walk preserves residue order, this kind of image encoding should generalize better than frequency-based representations to proteins whose discriminating signal lies in arrangement rather than composition.
  • The Monte Carlo Dropout scores give a per-sequence confidence estimate, so users can set a threshold and treat high-uncertainty calls as unknown rather than as confident positives.
  • Multi-class classification remains below the binary result, so the method's immediate use is the PVP/non-PVP decision rather than fine-grained protein-type labeling.
  • The uncertainty analysis shows that confidence is not uniform across protein classes and lengths, meaning a single global confidence threshold would be less reliable than a length- or class-aware one.

Reading between the lines

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

  • A natural stress test the paper leaves open: permute the amino-acid-to-color mapping and rerun the pipeline; if accuracy is insensitive to the permutation, the CNN may be reading global texture rather than the specific spatial encoding claimed.
  • Because the encoding machinery is not phage-specific, the approach could be ported to other protein annotation tasks where residue order matters, such as effector or secreted-protein classification.
  • The uncertainty estimates could be turned into an explicit rejection rule; one concrete extension is measuring how accuracy improves as high-entropy predictions are discarded.
  • The hand-set color and step-distance rules are arbitrary; a systematic search over those rules might reveal whether the reported accuracy is robust or tied to the particular choice.
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.

Referee Report

4 major / 3 minor

Summary. The paper introduces ProteoKnight, an image-based encoding for phage virion proteins built by adapting the DNA-walk algorithm with amino-acid-specific pixel colors and adjusted step distances, then classifying the resulting images with pretrained CNNs. The authors report 90.8% binary classification accuracy, claim this is comparable to state-of-the-art methods and surpasses frequency chaos game representation (FCGR), and present Monte Carlo Dropout variance/entropy as an uncertainty analysis. Multi-class classification is reported as suboptimal. The manuscript text as received is largely unreadable due to character-encoding corruption, which prevents full audit of the methods, tables, and figures.

Significance. If the encoding and results hold, the paper could offer a practical image-based route to phage virion protein prediction with an uncertainty flag, a useful contribution to computational virology. The central idea is plausible and the authors are candid that multi-class performance is weak. However, the current manuscript does not provide enough statistical detail to establish the claims: no dataset size, no error bars, no baseline table, no auditable encoding specification, and no validation that the uncertainty scores actually identify errors. The significance of the contribution is therefore currently unconfirmed.

major comments (4)
  1. [Abstract/Results] The sole performance claim is a single '90.8% accuracy' figure with no dataset size, class balance, train/test split, confidence interval, repeated-run variability, or external validation. The assertion in the abstract that this is 'comparable to state-of-the-art methods' and the conclusion that the method 'surpasses FCGR' are unsupported without a baseline table and controlled comparison on the same benchmark.
  2. [Methods, encoding description] The DNA-walk encoding is the load-bearing premise, but the exact mapping from amino acids to pixel colors and step sizes, the canvas geometry, and boundary handling are not specified in readable text; the full text is corrupted mojibake, so the mapping cannot be audited. No ablation, permutation test, or information-retention experiment is provided to show that the hand-set encoding preserves sequence or positional information in a way that generalizes beyond the benchmark. Without this, the reported accuracy and the claimed advantage over FCGR could be artifacts of the encoding hyperparameters rather than a genuine representational improvement.
  3. [Uncertainty Analysis] The MCD variance and entropy are descriptive statistics of the fitted model's predictive distribution, not an evaluation of uncertainty quality. The paper does not calibrate these scores against prediction error, does not test whether low-confidence predictions are statistically more likely to be wrong, and does not evaluate a rejection/flagging threshold. Therefore the conclusion that ProteoKnight 'identifies low-confidence predictions' is not validated.
  4. [Results, multi-class and length/class analysis] The paper admits multi-class accuracy is suboptimal, yet the abstract's overall statement of 'accurate and robust PVP predictions' is not qualified accordingly. The uncertainty findings across protein classes and sequence lengths appear to be presented without confidence intervals, error bars, or multiple runs, so observed differences may be noise.
minor comments (3)
  1. [Whole text] The submitted text is severely corrupted by character-encoding issues, making equations, tables, and figures unreadable. A corrected version is essential for any evaluation.
  2. [Data/code availability] No data availability or code availability statement is provided; this is needed for reproducibility.
  3. [Abstract/Introduction] The phrase 'spatial constraints in existing techniques' is not formally defined. The specific limitation of FCGR being addressed should be stated precisely and demonstrated with an example.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central accuracy claim is benchmarked against external PVP labels, and the encoding design is a hand-specified representation rather than a fitted prediction.

full rationale

The paper's main claim, 90.8% binary classification accuracy, is evaluated against external phage virion protein labels, so it is not an input reframed as an output. The ProteoKnight encoding ('adapts the classical DNA-Walk algorithm for protein sequences, incorporating pixel colors and adjusting walk distances') is a hand-designed representational choice, not a parameter fitted to the test labels; no equation or procedure in the readable abstract shows the encoding being optimized on the benchmark. The FCGR comparison is an empirical baseline comparison rather than a definitional equivalence. The Monte Carlo Dropout uncertainty analysis (variance and entropy measures) is descriptive of the trained model's own stochastic outputs; while it does not independently validate calibration, it is not a prediction forced by construction. The full text is largely corrupted mojibake, so the detailed encoding table and experimental protocol cannot be audited from the provided text; this is a transparency/reproducibility concern, not evidence of circularity. No self-citation chain, uniqueness theorem, or ansatz-via-citation is invoked in the accessible portions. Thus no circular step can be exhibited, and the appropriate score is 0.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The abstract forces the reader to accept four hand-set parameters (color map, walk distances, canvas geometry, MCD settings), five axioms (one standard, four domain assumptions), and zero invented entities. This is a typical ledger for an applied ML methods paper, but the absence of parameter values, dataset statistics, or code links in the abstract, plus the corrupted full text, means every item is accepted on faith at this stage.

free parameters (4)
  • amino-acid to pixel-color mapping
    Abstract Methods: 'incorporating pixel colors'; a design choice for 20 amino acids not derivable from biology, tuned to make the encoding informative.
  • per-residue walk distance rules
    Abstract Methods: 'adjusting walk distances'; step sizes are set by hand and are central to the claim of capturing protein features.
  • image canvas geometry (size, walk boundary handling)
    Required to feed variable-length protein walks into fixed-size pretrained CNNs; not specified in the abstract.
  • MCD settings (dropout rate, number of forward passes)
    Variance and entropy values depend on these; not specified in the abstract.
assumptions (5)
  • standard math The DNA-Walk construction (deterministic lattice path driven by alphabet symbols) is well defined for protein alphabets
    The method inherits this classical construction (Abstract, Methods); the mathematical validity of the walk itself is not in question.
  • domain assumption DNA-Walk style 2D traces encode biologically discriminative protein information
    The entire method adapts the classical DNA-Walk algorithm to proteins (Abstract, Methods); validity of the representational transfer is assumed.
  • domain assumption ImageNet-pretrained CNN features transfer to synthetic protein walk images
    Pretrained CNNs are used directly as classifiers (Abstract, Methods); transfer from photographs to synthetic colored traces is assumed.
  • domain assumption Monte Carlo Dropout approximates predictive uncertainty
    Variance and entropy across dropout forward passes are interpreted as prediction confidence (Abstract, Methods); a known finite-sample approximation.
  • domain assumption Benchmark labels and train/test splits are representative and contamination-free
    The 90.8% accuracy claim presumes the PVP benchmark sets and splits are standard and that no near-duplicate sequences leak across splits; unverifiable from the abstract.

how reviews work

0 comments
Cite this review

Pith. "Pith review of ProteoKnight: Convolution-based phage virion protein classification and uncertainty analysis." pith.science (2026). https://pith.science/paper/PDPLKTMJ

@misc{pith2026250807345,
  author       = {Pith},
  title        = {Pith review of: ProteoKnight: Convolution-based phage virion protein classification and uncertainty analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PDPLKTMJ}},
  note         = {Machine review of arXiv:2508.07345}
}
read the original abstract

\textbf{Introduction:} Accurate prediction of Phage Virion Proteins (PVP) is essential for genomic studies due to their crucial role as structural elements in bacteriophages. Computational tools, particularly machine learning, have emerged for annotating phage protein sequences from high-throughput sequencing. However, effective annotation requires specialized sequence encodings. Our paper introduces ProteoKnight, a new image-based encoding method that addresses spatial constraints in existing techniques, yielding competitive performance in PVP classification using pre-trained convolutional neural networks. Additionally, our study evaluates prediction uncertainty in binary PVP classification through Monte Carlo Dropout (MCD). \textbf{Methods:} ProteoKnight adapts the classical DNA-Walk algorithm for protein sequences, incorporating pixel colors and adjusting walk distances to capture intricate protein features. Encoded sequences were classified using multiple pre-trained CNNs. Variance and entropy measures assessed prediction uncertainty across proteins of various classes and lengths. \textbf{Results:} Our experiments achieved 90.8% accuracy in binary classification, comparable to state-of-the-art methods. Multi-class classification accuracy remains suboptimal. Our uncertainty analysis unveils variability in prediction confidence influenced by protein class and sequence length. \textbf{Conclusions:} Our study surpasses frequency chaos game representation (FCGR) by introducing novel image encoding that mitigates spatial information loss limitations. Our classification technique yields accurate and robust PVP predictions while identifying low-confidence predictions.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

68 extracted references · 65 canonical work pages

  1. [1]

    , Youle , M

    barticle Cobi \'a n G \"u emes , A.G. , Youle , M. , Cant \'u , V.A. , Felts , B. , Nulton , J. , Rohwer , F. : Viruses as winners in the game of life . Annual review of virology 3 , 197 -- 214 ( 2016 ) barticle

    work page 2016

  2. [2]

    , Nantasenamat , C

    barticle Kabir , M. , Nantasenamat , C. , Kanthawong , S. , Charoenkwan , P. , Shoombuatong , W. : Large-scale comparative review and assessment of computational methods for phage virion proteins identification . EXCLI journal 21 , 11 ( 2022 ) barticle

    work page 2022

  3. [3]

    , Alves Jr , N

    botherref Seguritan , V. , Alves Jr , N. , Arnoult , M. , Raymond , A. , Lorimer , D. , Burgin Jr , A.B. , Salamon , P. , Segall , A.M. : Artificial neural networks trained to detect viral and phage structural proteins (2012) botherref

    work page 2012

  4. [4]

    , Sullivan , M.B

    barticle Henn , M.R. , Sullivan , M.B. , Stange-Thomann , N. , Osburne , M.S. , Berlin , A.M. , Kelly , L. , Yandava , C. , Kodira , C. , Zeng , Q. , Weiand , M. , : Analysis of high-throughput sequencing and annotation strategies for phage genomes . PLoS One 5 ( 2 ), 9083 ( 2010 ) barticle

    work page 2010

  5. [5]

    : Uniprot.org

    botherref Consortium , U. : Uniprot.org. Accessed: September 15, 2024 (2002 -2024). https://www.uniprot.org/ botherref

    work page 2024

  6. [6]

    Nucleic Acids Research 51(D1), 523--531 (2023) botherref

    botherref Uniprot: the universal protein knowledgebase in 2023. Nucleic Acids Research 51(D1), 523--531 (2023) botherref

    work page 2023

  7. [7]

    , Zhang , J

    barticle Meng , C. , Zhang , J. , Ye , X. , Guo , F. , Zou , Q. : Review and comparative analysis of machine learning-based phage virion protein identification methods . Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics 1868 ( 6 ), 140406 ( 2020 ) barticle

    work page 2020

  8. [8]

    , Sacher , J.C

    barticle McCallin , S. , Sacher , J.C. , Zheng , J. , Chan , B.K. : Current state of compassionate phage therapy . Viruses 11 ( 4 ), 343 ( 2019 ) barticle

    work page 2019

Show all 68 references
  1. [9]

    , Yu , M

    barticle Wang , L.-F. , Yu , M. : Epitope identification and discovery using phage display libraries: applications in vaccine development and diagnostics . Current drug targets 5 ( 1 ), 1 -- 15 ( 2004 ) barticle

    2004

  2. [10]

    , Desiere , F

    barticle Br \"u ssow , H. , Desiere , F. : Comparative phage genomics and the evolution of siphoviridae: insights from dairy phages . Molecular microbiology 39 ( 2 ), 213 -- 223 ( 2001 ) barticle

    2001

  3. [11]

    , Hutinet , G

    barticle Menouni , R. , Hutinet , G. , Petit , M.-A. , Ansaldi , M. : Bacterial genome remodeling through bacteriophage recombination . FEMS microbiology letters 362 ( 1 ), 1 -- 10 ( 2015 ) barticle

    2015

  4. [12]

    , Ceyssens , P.-J

    botherref Lavigne , R. , Ceyssens , P.-J. , Robben , J. : Phage proteomics: applications of mass spectrometry. Bacteriophages: Methods and Protocols, Volume 2 Molecular and Applied Aspects, 239--251 (2009) botherref

    2009

  5. [13]

    , Gao , M

    barticle Yuan , Y. , Gao , M. : Proteomic analysis of a novel bacillus jumbo phage revealing glycoside hydrolase as structural component . Frontiers in Microbiology 7 , 745 ( 2016 ) barticle

    2016

  6. [14]

    , Shackelton , L.A

    barticle Duffy , S. , Shackelton , L.A. , Holmes , E.C. : Rates of evolutionary change in viruses: patterns and determinants . Nature Reviews Genetics 9 ( 4 ), 267 -- 276 ( 2008 ) barticle

    2008

  7. [15]

    , Ali , F

    barticle Arif , M. , Ali , F. , Ahmad , S. , Kabir , M. , Ali , Z. , Hayat , M. : Pred-bvp-unb: Fast prediction of bacteriophage virion proteins using un-biased multi-perspective properties with recursive feature elimination . Genomics 112 ( 2 ), 1565 -- 1574 ( 2020 ) barticle

    2020

  8. [16]

    , Li , Z

    barticle Zhang , Y. , Li , Z. : Rf\_phage virion: Classification of phage virion proteins with a random forest model . Frontiers in Genetics 13 , 1103783 ( 2023 ) barticle

    2023

  9. [17]

    , Ding , H

    botherref Feng , P.-M. , Ding , H. , Chen , W. , Lin , H. , et al.: Naive bayes classifier with feature selection to identify phage virion proteins. Computational and mathematical methods in medicine 2013 (2013) botherref

    2013

  10. [18]

    , Magnan , C.N

    barticle Galiez , C. , Magnan , C.N. , Coste , F. , Baldi , P. : Viralpro: a tool to identify viral capsid and tail sequences . Bioinformatics 32 ( 9 ), 1405 -- 1407 ( 2016 ) barticle

    2016

  11. [19]

    , Kim , J

    botherref Lee , J.S. , Kim , J. , Kim , P.M. : Score-based generative modeling for de novo protein design. Nature Computational Science, 1--11 (2023) botherref

    2023

  12. [20]

    , Frey , K

    barticle Jung , F. , Frey , K. , Zimmer , D. , M \"u hlhaus , T. : Deepstabp: a deep learning approach for the prediction of thermal protein stability . International Journal of Molecular Sciences 24 ( 8 ), 7444 ( 2023 ) barticle

    2023

  13. [21]

    , Yan , W

    barticle Zhao , J. , Yan , W. , Yang , Y. : Deeptp: a deep learning model for thermophilic protein prediction . International Journal of Molecular Sciences 24 ( 3 ), 2217 ( 2023 ) barticle

    2023

  14. [22]

    , Sharma , A

    barticle Manavi , F. , Sharma , A. , Sharma , R. , Tsunoda , T. , Shatabda , S. , Dehzangi , I. : Cnn-pred: Prediction of single-stranded and double-stranded dna-binding protein using convolutional neural networks . Gene 853 , 147045 ( 2023 ) barticle

    2023

  15. [23]

    , Peng , C

    barticle Shang , J. , Peng , C. , Tang , X. , Sun , Y. : Phavip: Phage virion protein classification based on chaos game representation and vision transformer . Bioinformatics 39 ( Supplement\_1 ), 30 -- 39 ( 2023 ) barticle

    2023

  16. [24]

    , Mohammadi , A

    barticle Akbari Rokn Abadi , S. , Mohammadi , A. , Koohi , S. : Walkim: Compact image-based encoding for high-performance classification of biological sequences using simple tuning-free cnns . Plos one 17 ( 4 ), 0267106 ( 2022 ) barticle

    2022

  17. [25]

    , Hinton , G

    barticle Srivastava , N. , Hinton , G. , Krizhevsky , A. , Sutskever , I. , Salakhutdinov , R. : Dropout: a simple way to prevent neural networks from overfitting . The journal of machine learning research 15 ( 1 ), 1929 -- 1958 ( 2014 ) barticle

    1929

  18. [26]

    , Tucker , A

    botherref Ghoshal , B. , Tucker , A. : Estimating uncertainty and interpretability in deep learning for coronavirus (covid-19) detection. arXiv preprint arXiv:2003.10769 (2020) botherref

    2003 arXiv

  19. [27]

    , Waegeman , W

    barticle H \"u llermeier , E. , Waegeman , W. : Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods . Machine Learning 110 , 457 -- 506 ( 2021 ) barticle

    2021

  20. [28]

    , Hwa Yang , Y

    barticle Yang , P. , Hwa Yang , Y. , B Zhou , B. , Y Zomaya , A. : A review of ensemble methods in bioinformatics . Current Bioinformatics 5 ( 4 ), 296 -- 308 ( 2010 ) barticle

    2010

  21. [29]

    , Deng , J

    barticle Russakovsky , O. , Deng , J. , Su , H. , Krause , J. , Satheesh , S. , Ma , S. , Huang , Z. , Karpathy , A. , Khosla , A. , Bernstein , M. , : Imagenet large scale visual recognition challenge . International journal of computer vision 115 , 211 -- 252 ( 2015 ) barticle

    2015

  22. [30]

    , Shlens , J

    bchapter Kornblith , S. , Shlens , J. , Le , Q.V. : Do better imagenet models transfer better? In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 2661 -- 2671 ( 2019 ) bchapter

    2019

  23. [31]

    , Qi , Z

    barticle Zhuang , F. , Qi , Z. , Duan , K. , Xi , D. , Zhu , Y. , Zhu , H. , Xiong , H. , He , Q. : A comprehensive survey on transfer learning . Proceedings of the IEEE 109 ( 1 ), 43 -- 76 ( 2020 ) barticle

    2020

  24. [32]

    , Pourpanah , F

    barticle Abdar , M. , Pourpanah , F. , Hussain , S. , Rezazadegan , D. , Liu , L. , Ghavamzadeh , M. , Fieguth , P. , Cao , X. , Khosravi , A. , Acharya , U.R. , : A review of uncertainty quantification in deep learning: Techniques, applications and challenges . Information fu...

    2021

  25. [33]

    , Ghahramani , Z

    bchapter Gal , Y. , Ghahramani , Z. : Dropout as a bayesian approximation: Representing model uncertainty in deep learning . In: International Conference on Machine Learning , pp. 1050 -- 1059 ( 2016 ). PMLR bchapter

    2016

  26. [34]

    , Gohel , S

    barticle Habehh , H. , Gohel , S. : Machine learning in healthcare . Current genomics 22 ( 4 ), 291 ( 2021 ) barticle

    2021

  27. [35]

    , Wang , F

    barticle Miotto , R. , Wang , F. , Wang , S. , Jiang , X. , Dudley , J.T. : Deep learning for healthcare: review, opportunities and challenges . Briefings in bioinformatics 19 ( 6 ), 1236 -- 1246 ( 2018 ) barticle

    2018

  28. [36]

    , Ayyappan , G

    barticle Kavitha , P. , Ayyappan , G. , Jayagopal , P. , Mathivanan , S.K. , Mallik , S. , Al-Rasheed , A. , Alqahtani , M.S. , Soufiene , B.O. : Detection for melanoma skin cancer through accf, bppf, and clf techniques with machine learning approach . BMC bioinformatics 24 ( ...

    2023

  29. [37]

    , Mazhar , T

    barticle Haq , I. , Mazhar , T. , Asif , R.N. , Ghadi , Y.Y. , Saleem , R. , Mallek , F. , Hamam , H. : A deep learning approach for the detection and counting of colon cancer cells (ht-29 cells) bunches and impurities . PeerJ Computer Science 9 , 1651 ( 2023 ) barticle

    2023

  30. [38]

    , Ye , Y

    barticle Elbasir , A. , Ye , Y. , Sch \"a ffer , D.E. , Hao , X. , Wickramasinghe , J. , Tsingas , K. , Lieberman , P.M. , Long , Q. , Morris , Q. , Zhang , R. , : A deep learning approach reveals unexplored landscape of viral expression in cancer . Nature communications 14 ( ...

    2023

  31. [39]

    , Bhushan , M

    barticle Rana , M. , Bhushan , M. : Machine learning and deep learning approach for medical image analysis: diagnosis to detection . Multimedia Tools and Applications 82 ( 17 ), 26731 -- 26769 ( 2023 ) barticle

    2023

  32. [40]

    , Peters , J.M

    barticle S \'a nchez Fern \'a ndez , I. , Peters , J.M. : Machine learning and deep learning in medicine and neuroimaging . Annals of the Child Neurology Society 1 ( 2 ), 102 -- 122 ( 2023 ) barticle

    2023

  33. [41]

    , Nazir , M.B

    barticle Hussain , I. , Nazir , M.B. , : Mind matters: Exploring ai, machine learning, and deep learning in neurological health . International Journal of Advanced Engineering Technologies and Innovations 1 ( 4 ), 209 -- 230 ( 2024 ) barticle

    2024

  34. [42]

    , Zhang , T

    botherref Hu , N. , Zhang , T. , Wu , Y. , Tang , B. , Li , M. , Song , B. , Gong , Q. , Wu , M. , Gu , S. , Lui , S. : Detecting brain lesions in suspected acute ischemic stroke with ct-based synthetic mri using generative adversarial networks. Annals of Translational Medicin...

    2022

  35. [43]

    , Read , R.C

    botherref Theodosiou , A.A. , Read , R.C. : Artificial intelligence, machine learning and deep learning: Potential resources for the infection clinician. Journal of Infection (2023) botherref

    2023

  36. [44]

    , Ramadan , N

    barticle Badawy , M. , Ramadan , N. , Hefny , H.A. : Healthcare predictive analytics using machine learning and deep learning techniques: a survey . Journal of Electrical Systems and Information Technology 10 ( 1 ), 40 ( 2023 ) barticle

    2023

  37. [45]

    , Uddin , J

    barticle Raza , A. , Uddin , J. , Almuhaimeed , A. , Akbar , S. , Zou , Q. , Ahmad , A. : Aips-sntcn: Predicting anti-inflammatory peptides using fasttext and transformer encoder-based hybrid word embedding with self-normalized temporal convolutional networks . Journal of chem...

    2023

  38. [46]

    , Raza , A

    barticle Akbar , S. , Raza , A. , Zou , Q. : Deepstacked-avps: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model . BMC bioinformatics 25 ( 1 ), 102 ( 2024 ) barticle

    2024

  39. [47]

    , Ofer , D

    barticle Brandes , N. , Ofer , D. , Peleg , Y. , Rappoport , N. , Linial , M. : Proteinbert: a universal deep-learning model of protein sequence and function . Bioinformatics 38 ( 8 ), 2102 -- 2110 ( 2022 ) barticle

    2022

  40. [48]

    , Evans , R

    barticle Senior , A.W. , Evans , R. , Jumper , J. , Kirkpatrick , J. , Sifre , L. , Green , T. , Qin , C. , Z \' dek , A. , Nelson , A.W. , Bridgland , A. , : Improved protein structure prediction using potentials from deep learning . Nature 577 ( 7792 ), 706 -- 710 ( 2020 ) barticle

    2020

  41. [49]

    , Imeni , N

    barticle Nami , Y. , Imeni , N. , Panahi , B. : Application of machine learning in bacteriophage research . BMC microbiology 21 ( 1 ), 193 ( 2021 ) barticle

    2021

  42. [50]

    , Imeni , N

    barticle Nami , Y. , Imeni , N. , Panahi , B. : Application of machine learning in bacteriophage research . BMC microbiology 21 ( 1 ), 1 -- 8 ( 2021 ) barticle

    2021

  43. [51]

    , Zhou , H

    barticle Fang , Z. , Zhou , H. : Virionfinder: identification of complete and partial prokaryote virus virion protein from virome data using the sequence and biochemical properties of amino acids . Frontiers in microbiology 12 , 615711 ( 2021 ) barticle

    2021

  44. [52]

    , Salamon , P

    barticle Cantu , V.A. , Salamon , P. , Seguritan , V. , Redfield , J. , Salamon , D. , Edwards , R.A. , Segall , A.M. : Phanns, a fast and accurate tool and web server to classify phage structural proteins . PLOS Computational Biology 16 ( 11 ), 1 -- 18 ( 2020 ) 10.1371/journa...

    2020 doi

  45. [53]

    , Feng , T

    barticle Fang , Z. , Feng , T. , Zhou , H. , Chen , M. : Deepvp: Identification and classification of phage virion proteins using deep learning . GigaScience 11 , 076 ( 2022 ) barticle

    2022

  46. [54]

    , Akbar , S

    barticle Ullah , M. , Akbar , S. , Raza , A. , Zou , Q. : Deepavp-tppred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm . Bioinformatics 40 ( 5 ), 305 ( 2024 ) barticle

    2024

  47. [55]

    , Zou , Q

    barticle Akbar , S. , Zou , Q. , Raza , A. , Alarfaj , F.K. : iafps-mv-bitcn: Predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks . Artificial Intellig...

    2024

  48. [56]

    , Mitra , S.K

    barticle Berger , J.A. , Mitra , S.K. , Carli , M. , Neri , A. : Visualization and analysis of dna sequences using dna walks . Journal of the Franklin Institute 341 ( 1-2 ), 37 -- 53 ( 2004 ) barticle

    2004

  49. [57]

    , : Learning deep architectures for ai

    barticle Bengio , Y. , : Learning deep architectures for ai . Foundations and trends in Machine Learning 2 ( 1 ), 1 -- 127 ( 2009 ) barticle

    2009

  50. [58]

    , Aly , M.H

    barticle Salama , W.M. , Aly , M.H. : Deep learning in mammography images segmentation and classification: Automated cnn approach . Alexandria Engineering Journal 60 ( 5 ), 4701 -- 4709 ( 2021 ) barticle

    2021

  51. [59]

    , Wang , Z

    barticle Rawat , W. , Wang , Z. : Deep convolutional neural networks for image classification: A comprehensive review . Neural computation 29 ( 9 ), 2352 -- 2449 ( 2017 ) barticle

    2017

  52. [60]

    , Dao , F.-Y

    barticle Tan , J.-X. , Dao , F.-Y. , Lv , H. , Feng , P.-M. , Ding , H. : Identifying phage virion proteins by using two-step feature selection methods . Molecules 23 ( 8 ), 2000 ( 2018 ) barticle

    2000

  53. [61]

    Accessed: 2024-07-27

    botherref torchvision : Source Code for Torchvision.models.googlenet. Accessed: 2024-07-27. https://pytorch.org/vision/main/_modules/torchvision/models/googlenet.html#GoogLeNet_Weights botherref

    2024

  54. [62]

    , Nantasenamat , C

    barticle Charoenkwan , P. , Nantasenamat , C. , Hasan , M.M. , Shoombuatong , W. : Meta-ipvp: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation . Journal of Computer-Aided Molecular Design 34 , 1105 -- ...

    2020

  55. [63]

    , Kanthawong , S

    barticle Charoenkwan , P. , Kanthawong , S. , Schaduangrat , N. , Yana , J. , Shoombuatong , W. : Pvpred-scm: improved prediction and analysis of phage virion proteins using a scoring card method . Cells 9 ( 2 ), 353 ( 2020 ) barticle

    2020

  56. [64]

    , Trujillo Codorni \'u , R

    barticle Milan \'e s-Hermosilla , D. , Trujillo Codorni \'u , R. , L \'o pez-Baracaldo , R. , Sagar \'o -Zamora , R. , Delisle-Rodriguez , D. , Villarejo-Mayor , J.J. , N \'u \ n ez- \'A lvarez , J.R. : Monte carlo dropout for uncertainty estimation and motor imagery classific...

    2021

  57. [65]

    , Gal , Y

    botherref Kendall , A. , Gal , Y. : What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems 30 (2017) botherref

    2017

  58. [66]

    : Entropy (for data science) (2021)

    botherref Starmer , J. : Entropy (for data science) (2021). https://youtu.be/YtebGVx-Fxw?si=bjQc1IKhNgXo45ND botherref

    2021

  59. [67]

    , Dehghani , M

    botherref Tay , Y. , Dehghani , M. , Gupta , J. , Bahri , D. , Aribandi , V. , Qin , Z. , Metzler , D. : Are pre-trained convolutions better than pre-trained transformers? arXiv preprint arXiv:2105.03322 (2021) botherref

    2021 arXiv

  60. [68]

    write newline

    " write newline "" before.all 'output.state := FUNCTION string.to.integer 't := t text.length 'k := #1 'char.num := t char.num #1 substring 's := s is.num s "." = or char.num k = not and char.num #1 + 'char.num := while char.num #1 - 'char.num := t #1 char.num substring FUNCTI...

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.