Paper Citation Record · LEDGER

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data

As of 19 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 2 inbound Pith citation observations for arXiv:2504.16956.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2504.16956 v4

Coverage vector

measured 53 of 53 reference resolution

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measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:20:19.712971Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-10T21:50:44.094945Z

Reference resolution

53 of 53 outbound references displayed

verified exact2
verified fuzzy32
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ac478c12-59b5-41fb-abc2-cef7c9d884f1 · outbound

This paper cites scmulan: a multitask generative pre-trained lan- guage model for single-cell analysis.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data scmulan: a multitask generative pre-trained lan- guage model for single-cell analysis

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.481388Z

Source-reported events for the cited work

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Observation 0e1573a4-b714-496c-b344-9adee5e79162 · outbound

This paper cites A deep dive into single-cell rna sequencing foundation models.bioRxiv, pages 2023–10, 2023.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data A deep dive into single-cell rna sequencing foundation models.bioRxiv, pages 2023–10, 2023

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.465059Z

Source-reported events for the cited work

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Observation eb7182d3-2c91-434b-af6d-0d8175ee0e67 · outbound

This paper cites Transformer for one stop interpretable cell type annotation.Nature Communications, 14(1):223, 2023.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Transformer for one stop interpretable cell type annotation.Nature Communications, 14(1):223, 2023

Reference 3

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Source-reported events for the cited work

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Observation b33518a9-5adf-438d-b8db-964a462f6689 · outbound

This paper cites Rethinking Attention with Performers.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Rethinking Attention with Performers

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T11:14:54.382932Z

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Observation 9c1e3a9c-a2c2-47d7-ad5c-6fe3bbc01658 · outbound

This paper cites scgpt: toward building a foundation model for single-cell multi-omics using generative ai.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data scgpt: toward building a foundation model for single-cell multi-omics using generative ai

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.440275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d4da2ed6-791f-4038-b7d8-87c8339d0c61 · outbound

This paper cites White-Box Diffusion Transformer for single-cell RNA-seq generation.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data White-Box Diffusion Transformer for single-cell RNA-seq generation

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:14:54.832322Z

Source-reported events for the cited work

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Observation 31fe3dec-e67a-4d7b-aece-8302d499ef07 · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness.Advances in Neural Information Processing Systems, 35:16344–16359, 2022.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Flashattention: Fast and memory-efficient exact attention with io-awareness.Advances in Neural Information Processing Systems, 35:16344–16359, 2022

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T11:14:54.399526Z

Source-reported events for the cited work

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Observation 004fd6b0-c3e0-44ba-b153-3deba0823832 · outbound

This paper cites Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T11:14:54.404499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c506856c-eafd-48d1-8f75-10c90461e4b4 · outbound

This paper cites Recent advances in trajectory inference from single-cell omics data.Current Opinion in Systems Biology, 27:100344, 2021.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Recent advances in trajectory inference from single-cell omics data.Current Opinion in Systems Biology, 27:100344, 2021

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.402194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 64218a46-b7b3-45dd-978c-f290c7ce1ddd · outbound

This paper cites scRDiT: Generating single-cell RNA-seq data by diffusion transformers and accelerating sampling.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data scRDiT: Generating single-cell RNA-seq data by diffusion transformers and accelerating sampling

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:14:54.809405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1cf06c78-dceb-4cea-bab8-b6065f6d411f · outbound

This paper cites Gene2vec: distributed representation of genes based on co-expression.BMC genomics, 20:7–15, 2019.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Gene2vec: distributed representation of genes based on co-expression.BMC genomics, 20:7–15, 2019

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.386332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3965e47d-f00b-4f90-965d-7c64aa4442e2 · outbound

This paper cites scgraphformer: unveiling cellular heterogeneity and interactions in scrna-seq data using a scalable graph transformer network.Communications Biology, 7(1):1463, 2024.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data scgraphformer: unveiling cellular heterogeneity and interactions in scrna-seq data using a scalable graph transformer network.Communications Biology, 7(1):1463, 2024

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.370223Z

Source-reported events for the cited work

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Observation 899d7a06-1447-4c76-bfeb-5359ff5b28a4 · outbound

This paper cites Pathway analysis: state of the art.Frontiers in physiology, 6:383, 2015.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Pathway analysis: state of the art.Frontiers in physiology, 6:383, 2015

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.353061Z

Source-reported events for the cited work

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Observation beec5c8d-7046-475c-9e4a-98d994a90dd5 · outbound

This paper cites xtrimogene: an efficient and scalable representation learner for single-cell rna-seq data.Advances in Neural Information Processing Systems, 36, 2024.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data xtrimogene: an efficient and scalable representation learner for single-cell rna-seq data.Advances in Neural Information Processing Systems, 36, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.337876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c3bce6f7-0dcb-4ac1-9d23-9b119755adc8 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T11:14:54.442902Z

Source-reported events for the cited work

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Observation ce234686-d8ba-4b32-a33c-2baeac3c8495 · outbound

This paper cites Integrating pathway knowledge with deep neural networks to reduce the dimen- sionality in single-cell rna-seq data.BioData Mining, 15:1–21, 2022.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Integrating pathway knowledge with deep neural networks to reduce the dimen- sionality in single-cell rna-seq data.BioData Mining, 15:1–21, 2022

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.321585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f7bbb6a8-5d51-4eee-981b-227ef16d4432 · outbound

This paper cites Large-scale foundation model on single-cell transcriptomics.Nature Methods, pages 1–11, 2024.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Large-scale foundation model on single-cell transcriptomics.Nature Methods, pages 1–11, 2024

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.302490Z

Source-reported events for the cited work

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Observation 5d0b5af7-a05d-40f8-9156-e88b5b05ecc8 · outbound

This paper cites sctranssort: Transformers for intelligent annotation of cell types by gene embeddings.Biomolecules, 13(4):611, 2023.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data sctranssort: Transformers for intelligent annotation of cell types by gene embeddings.Biomolecules, 13(4):611, 2023

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.283018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e8b61f3b-94e9-4477-8854-9973e1dce5ab · outbound

This paper cites Assessing the limits of zero-shot foundation models in single-cell biology.bioRxiv, pages 2023–10, 2023.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Assessing the limits of zero-shot foundation models in single-cell biology.bioRxiv, pages 2023–10, 2023

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.265679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3de71a73-3400-4d74-a435-0b150f36b763 · outbound

This paper cites Fast, sensitive and accurate integration of single-cell data with harmony.Nature methods, 16(12):1289–1296, 2019.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Fast, sensitive and accurate integration of single-cell data with harmony.Nature methods, 16(12):1289–1296, 2019

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.249823Z

Source-reported events for the cited work

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Observation a8f399fc-3c15-459f-886b-6ca256f57b98 · outbound

This paper cites Single-cell rna sequencing in cancer research: New insights and applications.Cancer Research, 84(10):1234–1245, 2024.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Single-cell rna sequencing in cancer research: New insights and applications.Cancer Research, 84(10):1234–1245, 2024

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.233390Z

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Observation 3c5ba091-94cf-49fc-841b-98a5fac79900 · outbound

This paper cites Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting

Reference 22

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Observation 046f11bd-2de5-4055-92d5-70aaa731d2aa · outbound

This paper cites SIGMA: Selective Gated Mamba for Sequential Recommendation.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data SIGMA: Selective Gated Mamba for Sequential Recommendation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T11:14:54.483631Z

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Observation 01003314-0768-4e50-a61c-1e3ae014fcdc · outbound

This paper cites Exploring genetic interaction manifolds constructed from rich single-cell phenotypes.Science, 365(6455):786–793, 2019.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Exploring genetic interaction manifolds constructed from rich single-cell phenotypes.Science, 365(6455):786–793, 2019

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T11:14:54.488857Z

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Observation c5d7e4cf-50c8-4f82-a2fb-a374d2231962 · outbound

This paper cites Single-cell rna-seq data augmentation using generative fourier transformer.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Single-cell rna-seq data augmentation using generative fourier transformer

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T11:14:54.493836Z

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Observation 8aa1f657-9f53-4265-9683-c9f03419c625 · outbound

This paper cites scHyena: Foundation Model for Full-Length Single-Cell RNA-Seq Analysis in Brain.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data scHyena: Foundation Model for Full-Length Single-Cell RNA-Seq Analysis in Brain

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T11:14:54.499057Z

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Observation 0e8fe0ec-9ec2-4a3f-964b-d7d2a15c8500 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Representation Learning with Contrastive Predictive Coding

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T11:14:54.503962Z

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Observation 1b1332bd-8e0a-4857-8de5-2f84c5f7e53f · outbound

This paper cites Machine learning and statistical methods for clustering single-cell rna-sequencing data.Briefings in bioinformatics, 21(4):1209–1223, 2020.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Machine learning and statistical methods for clustering single-cell rna-sequencing data.Briefings in bioinformatics, 21(4):1209–1223, 2020

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T11:14:54.509455Z

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Observation 33abf6c8-3904-4101-9601-a9855b37eadc · outbound

This paper cites Integration of single-cell rna-seq datasets: a review of computational methods.Molecules and cells, 46(2):106–119, 2023.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Integration of single-cell rna-seq datasets: a review of computational methods.Molecules and cells, 46(2):106–119, 2023

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.183418Z

Source-reported events for the cited work

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Observation c01f3b58-b4b7-4044-87fc-298d831c37b0 · outbound

This paper cites Regenne: genetic pathway-based deep neural network using canonical correlation regularizer for disease prediction.Bioinformatics, 39(11):btad679, 2023.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Regenne: genetic pathway-based deep neural network using canonical correlation regularizer for disease prediction.Bioinformatics, 39(11):btad679, 2023

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.166200Z

Source-reported events for the cited work

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Observation e243e692-fb58-43ba-9bf8-3ef8a865f00e · outbound

This paper cites Generative pretraining from large-scale transcriptomes for single-cell deciphering.Iscience, 26(5), 2023.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Generative pretraining from large-scale transcriptomes for single-cell deciphering.Iscience, 26(5), 2023

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.150304Z

Source-reported events for the cited work

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Observation ffbf0acd-c41a-4eb2-803b-3e588edeed12 · outbound

This paper cites A universal approach for integrating super large-scale single- cell transcriptomes by exploring gene rankings.Briefings in Bioinformatics, 23(2):bbab573, 2022.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data A universal approach for integrating super large-scale single- cell transcriptomes by exploring gene rankings.Briefings in Bioinformatics, 23(2):bbab573, 2022

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.135209Z

Source-reported events for the cited work

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Observation c1f2e593-ce0f-4b5c-97e2-5241ff036bdf · outbound

This paper cites Bidirectional mamba with dual-branch feature extraction for hyperspectral image classification.Sensors, 24(21):6899, 2024.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Bidirectional mamba with dual-branch feature extraction for hyperspectral image classification.Sensors, 24(21):6899, 2024

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.118966Z

Source-reported events for the cited work

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Observation 4ffcf877-be8c-4d34-8169-d6fd0f61a5bb · outbound

This paper cites Transformers in single-cell omics: a review and new perspectives.Nature methods, 21(8):1430–1443, 2024.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Transformers in single-cell omics: a review and new perspectives.Nature methods, 21(8):1430–1443, 2024

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.102319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 26a9c566-0a09-4486-b5b2-36dfb1132c0e · outbound

This paper cites Transfer learning enables predictions in network biology.Nature, 618(7965):616–624, 2023.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Transfer learning enables predictions in network biology.Nature, 618(7965):616–624, 2023

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-16T11:14:54.542306Z

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Observation 83e5fa78-8924-4083-bd02-4ec9a734c97d · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T11:14:54.547161Z

Source-reported events for the cited work

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Observation 5035d392-ea75-4369-ae9b-f383f9647ca8 · outbound

This paper cites Cellplm: pre-training of cell language model beyond single cells.bioRxiv, pages 2023–10, 2023.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Cellplm: pre-training of cell language model beyond single cells.bioRxiv, pages 2023–10, 2023

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.065953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d3253648-0743-4ad2-b02f-b8968825d1c9 · outbound

This paper cites Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-16T11:14:54.557468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6d681a8-e15a-4bf8-b965-271c9f97e86b · outbound

This paper cites A comprehensive review of computational methods for scrna-seq.Briefings in Bioinformatics, 25(6):789–799, 2024.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data A comprehensive review of computational methods for scrna-seq.Briefings in Bioinformatics, 25(6):789–799, 2024

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.050263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5b4b5c5a-cffb-48cf-a73c-a81600d0c34f · outbound

This paper cites scclip: Multi-modal single-cell contrastive learning integration pre-training.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data scclip: Multi-modal single-cell contrastive learning integration pre-training

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.033430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b8156ec1-fbb5-408e-b8cb-4f596d5ccb43 · outbound

This paper cites Stgrns: an interpretable transformer- based method for inferring gene regulatory networks from single-cell transcriptomic data.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Stgrns: an interpretable transformer- based method for inferring gene regulatory networks from single-cell transcriptomic data

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:55.017797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0d8d4c00-9974-48ed-93a0-b1fd4f0e2844 · outbound

This paper cites scbert as a large-scale pretrained deep language model for cell type annotation of single-cell rna-seq data.Nature Machine Intelligence, 4(10):852–866, 2022.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data scbert as a large-scale pretrained deep language model for cell type annotation of single-cell rna-seq data.Nature Machine Intelligence, 4(10):852–866, 2022

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-16T11:14:54.577844Z

Source-reported events for the cited work

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Observation df64fc24-c100-4e3a-b5d1-f70693b82a78 · outbound

This paper cites Genecompass: deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model.Cell Research, pages 1–16, 2024.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Genecompass: deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model.Cell Research, pages 1–16, 2024

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:54.991597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 526787d5-00f2-4a71-989d-183f02f67030 · outbound

This paper cites sctca: a hybrid transformer-cnn architecture for imputation and denoising of scdna-seq data.Briefings in Bioinformatics, 25(6):bbae577, 2024.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data sctca: a hybrid transformer-cnn architecture for imputation and denoising of scdna-seq data.Briefings in Bioinformatics, 25(6):bbae577, 2024

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:54.976553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5e325757-b8bd-4198-8956-6b14fdd22be9 · outbound

This paper cites Innovative super-resolution in spatial transcriptomics: a transformer model exploiting histology images and spatial gene expression.Briefings in Bioinformatics, 25(2):bbae052, 2024.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Innovative super-resolution in spatial transcriptomics: a transformer model exploiting histology images and spatial gene expression.Briefings in Bioinformatics, 25(2):bbae052, 2024

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:54.960968Z

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Observation c3ed95d4-3e32-4b3e-b485-3735a873917e · outbound

This paper cites Large-Scale Cell Representation Learning via Divide-and-Conquer Contrastive Learning.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data Large-Scale Cell Representation Learning via Divide-and-Conquer Contrastive Learning

Reference 46

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Observation bfa74aac-ea7d-4df2-b19a-809c416efb89 · outbound

This paper cites LangCell: Language-Cell Pre-training for Cell Identity Understanding.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data LangCell: Language-Cell Pre-training for Cell Identity Understanding

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-16T11:14:54.602211Z

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This paper cites in some cases, data from the same cell exists in different datasets, therefore cells can be duplicated throughout CELLxGENE Discover and by extension the Census,.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data in some cases, data from the same cell exists in different datasets, therefore cells can be duplicated throughout CELLxGENE Discover and by extension the Census,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:54.944435Z

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Observation 6bbe74c8-0ea2-4150-bd27-e824552cf7c7 · outbound

This paper cites By adjusting for chance agreement, ARIcell captures how well the integration maintains the clustering structure: ARIcell = Index observed−Index expected Max index−Index expected.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data By adjusting for chance agreement, ARIcell captures how well the integration maintains the clustering structure: ARIcell = Index observed−Index expected Max index−Index expected

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:54.927514Z

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Observation 54a9f61c-feca-4436-ab37-ca32f96268e6 · outbound

This paper cites This score ranges from 0 (no alignment) to 1 (perfect alignment), and we use it to assess the consistency of clustering.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data This score ranges from 0 (no alignment) to 1 (perfect alignment), and we use it to assess the consistency of clustering

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:54.911397Z

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Observation bc144887-1bfe-40ef-b952-51465005f06f · outbound

This paper cites The silhouette score (ASWC) evaluates whether cells are closer to their own cluster than to other clusters.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data The silhouette score (ASWC) evaluates whether cells are closer to their own cluster than to other clusters

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:54.896552Z

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Observation a7e2d5c9-1a90-4839-b518-e23d4c039fd6 · outbound

This paper cites First, we calculate the silhouette score based on batch labels (ASWB), which measures how batch-specific artifacts affect the integrated space.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data First, we calculate the silhouette score based on batch labels (ASWB), which measures how batch-specific artifacts affect the integrated space

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:14:54.881425Z

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Observation 5acdee3b-f7a9-4c22-ba23-6c13af5af9b5 · outbound

This paper cites positive.

GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data positive

Reference 53

Resolution: malformed identifier; raw_fallback, observed 2026-08-16T11:14:54.864018Z

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Pith citing papers

Observation 099b521b-51e3-4407-82d0-b03817cfde76 · inbound

bioMoR: Biology-Guided Mixture-of-Recursions for Effective Genomic Learning cites this paper.

bioMoR: Biology-Guided Mixture-of-Recursions for Effective Genomic Learning GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data

Reference 38

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T21:50:44.101762Z

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Observation 2b6cabd2-adfe-4c3e-82db-bb79d781d7a1 · inbound

bioMoR: Biology-Guided Mixture-of-Recursions for Effective Genomic Learning cites this paper.

bioMoR: Biology-Guided Mixture-of-Recursions for Effective Genomic Learning GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T04:20:19.712971Z

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