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HiC4D: forecasting spatiotemporal Hi-C data with residual ConvLSTM

Briefings in Bioinformatics(2023)10.1093/bib/bbad263Source: DataRank Database

HiC4D: forecasting spatiotemporal Hi-C data with residual ConvLSTM is a research paper published in Briefings in Bioinformatics (2023). On theSindex it has a DataRank of 0. It has been cited 5 times.

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Open Access5 citations

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Enrichment:Funding (3 grants)OA: hybridTopics (18)IDs (PubMed, PMC)Annotations (3)

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Authors (2)

Zheng Wang ORCID,Tong Liu ORCID

Live enrichment

PubMed:37478379 PMC:PMC10516390

National Institutes of Health

Grant: R35GM137974

National Institutes of Health

Grant: 5R35GM137974-05

Advanced algorithms to infer and analyze 3D genome structures

National Institute of General Medical Sciences

hybridLicense: cc-by

publisherhttps://academic.oup.com/bib/article-pdf/doi/10.1093/bib/bbad263/50925989/bbad263.pdf

publisherhttps://academic.oup.com/bib/article-pdf/24/5/bbad263/51710819/bbad263.pdf

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/10516390

Europe_PMChttps://europepmc.org/articles/PMC10516390

Europe_PMChttps://europepmc.org/articles/PMC10516390?pdf=render

Fields of Study

0301 basic medicine0303 health sciences03 medical and health sciences

MeSH Terms

AnimalsMiceGenomicsGenomeForecasting

Keywords

Deep Learning4D GenomePredicting Spatiotemporal Hi-c DataConvolutional Long Short-term MemoryMiceGenomeProblem Solving ProtocolAnimalsGenomicsForecasting

HiC4D: forecasting spatiotemporal Hi-C data with residual ConvLSTM

FAIR Checklist

Authors (2)

Funding (3 grants)

Open Access

Topics

Annotations (Europe PMC)