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Monod : model-based discovery and integration through fitting stochastic transcriptional dynamics to single-cell sequencing data

bioRxiv (Cold Spring Harbor Laboratory)(2022)10.1101/2022.06.11.495771Source: DataRank Database

Monod : model-based discovery and integration through fitting stochastic transcriptional dynamics to single-cell sequencing data is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2022). On theSindex it has a DataRank of 0. It has been cited 18 times.

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Open Access18 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (4 grants)OA: greenTopics (15)IDs (PubMed)

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Findable (1/2)

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Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (5)

Tara Chari ORCID,Maria Carilli ORCID,John J. Vastola ORCID,Lior Pachter ORCID,Gennady Gorin ORCID

Live enrichment

PubMed:19162232

NIH

Grant: U19MH114830

NIH

Grant: 5UM1HG012077-02

Center for Mouse Genomic Variation at Single Cell Resolution

NSF GRFP

Grant: 2139433

National Institutes of Health

Grant: 5U19MH114830-05

A comprehensive whole-brain atlas of cell types in the mouse

greenLicense: cc-by

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2022/06/12/2022.06.11.495771.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2022.06.11.495771

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/12637513

repositoryhttps://resolver.caltech.edu/CaltechAUTHORS:20220614-221628000

https://doi.org/10.1038/s41592-025-02832-x

Fields of Study

0301 basic medicine0303 health sciences03 medical and health sciences

Keywords

Stochastic ProcessesTranscription, GeneticSequence Analysis, RNAGene Expression ProfilingArticleComputational biophysicsGene Expression RegulationComputational modelsHumansSingle-Cell AnalysisTranscriptomicsSoftware