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SAILER: Scalable and Accurate Invariant Representation Learning for Single-Cell ATAC-Seq Processing and Integration

bioRxiv (Cold Spring Harbor Laboratory)(2021)10.1101/2021.01.28.428689Source: DataRank Database

SAILER: Scalable and Accurate Invariant Representation Learning for Single-Cell ATAC-Seq Processing and Integration is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2021). On theSindex it has a DataRank of 0. It has been cited 5 times.

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Enrichment:Funding (4 grants)OA: greenTopics (45)IDs (PubMed)

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Authors (7)

Laiyi Fu ORCID,Jie Wu ORCID,Qinke Peng ORCID,Qing Nie ORCID,Jing Zhang ORCID

Live enrichment

PubMed:34252968

National Science Foundation

Grant: 1715017

III: Small: Integrating and Interpreting Heterogeneous Genomic Data Through Deep Learning

National Institutes of Health

Grant: 5K01MH123896-02

A big data approach to explore epigenetic heterogeneity and interpret noncoding variants for psychiatric disorders

National Science Foundation

Grant: 1763272

NSF-Simons Center for Multiscale Cell Fate Research

National Institutes of Health

Grant: 5U54CA217378-05

Complexity, Cooperation and Community in Cancer

greenLicense: cc-by-nc

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2021/01/31/2021.01.28.428689.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2021.01.28.428689

https://doi.org/10.1093/bioinformatics/btab303

https://academic.oup.com/bioinformatics/article-pdf/37/Supplement_1/i317/39620122/btab303.pdf

https://doi.org/10.1101/2021.01.28.428689

Fields of Study

0301 basic medicine0206 medical engineering02 engineering and technology03 medical and health sciences

Keywords

EpigenomicsChromatin Immunoprecipitation Sequencing (mesh)Bioinformatics1.1 Normal biological development and functioning3102 Bioinformatics and Computational Biology (for-2020)Bioinformatics and Computational BiologyTransposasesBioengineering08 Information and Computing Sciences (for)Mathematical SciencesMachine Learning46 Information and Computing Sciences (for-2020)Information and Computing Sciences4611 Machine Learning (for-2020)31 Biological sciences (for-2020)GeneticsSoftware (mesh)1.1 Normal biological development and functioning (hrcs-rac)Regulatory and Functional GenomicsNetworking and Information Technology R&D (NITRD) (rcdc)Genetics (rcdc)Sequence Analysis, RNAHuman GenomeBioengineering (rcdc)Biological SciencesHuman Genome (rcdc)49 Mathematical sciences (for-2020)00406 Biological Sciences (for)Epigenomics (mesh)Single-Cell Analysis (mesh)Networking and Information Technology R&D (NITRD)Bioinformatics (science-metrix)01 Mathematical Sciences (for)RNARNA (mesh)Chromatin Immunoprecipitation SequencingTransposases (mesh)Single-Cell AnalysisSequence AnalysisSoftware