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Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets

Patterns(2022)10.1016/j.patter.2022.100473Source: DataRank Database

Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets is a research paper published in Patterns (2022). On theSindex it has a DataRank of 0. It has been cited 15 times.

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Enrichment:Funding (12 grants)OA: goldTopics (7)IDs (PubMed, PMC)Annotations (1)

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Authors (8)

Tyler Lovelace ORCID,Florentina Bunea ORCID,Marten Wegkamp ORCID,Sudhir Pai Kasturi ORCID,Harinder Singh ORCID

Live enrichment

PubMed:35607614 PMC:PMC9122954

National Science Foundation

Grant: DMS-1712709

National Science Foundation

Grant: DMS-2015195

National Institutes of Health

Grant: DP2AI164325

National Institutes of Health

Grant: F31LM013966

National Institutes of Health

Grant: R01HL140963

National Institutes of Health

Grant: R01HL157879

National Institutes of Health

Grant: R01HL159805

National Institutes of Health

Grant: U01AI141990

National Institutes of Health

Grant: U01HL137159

U.S. Department of Defense

Grant: W81XWH2110864

NIH HHS

Grant: P51 OD011132

Université Pierre et Marie Curie

goldLicense: cc-by-nc-nd

publisherhttp://www.cell.com/article/S2666389922000538/pdf

publisherhttps://api.elsevier.com/content/article/PII:S2666389922000538?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S2666389922000538?httpAccept=text/plain

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/9122954

Europe_PMChttps://europepmc.org/articles/PMC9122954

Keywords

systems biologyMachine LearningCausal InferenceDimensionality ReductionLatent ModelSystems ImmunologyInterpretable Machine Learning

Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets

FAIR Checklist

Authors (8)

Funding (12 grants)

Open Access

Topics

Annotations (Europe PMC)