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Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma

bioRxiv (Cold Spring Harbor Laboratory)(2024)10.1101/2024.06.25.600585Source: DataRank Database

Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2024). On theSindex it has a DataRank of 0. It has been cited 1 time.

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Open Access1 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (2 grants)OA: greenTopics (34)IDs (PubMed, OpenAlex)SDGs (1)

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Open Access

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Authors (7)

Hardy Bright,Yuan Pu ORCID,Merih Deniz Toruner ORCID,Jordan Idehen,Nikos Tapinos ORCID

Live enrichment

PubMed:38979226 OpenAlex:https://openalex.org/W4400159090

National Institutes of Health

Grant: 1R35HG011939-01

Deep learning for understanding gene regulation in diseases via 'omics' integration

NHGRI NIH HHS

Grant: R35 HG011939

greenLicense: cc-by

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2024/06/28/2024.06.25.600585.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2024.06.25.600585

repositoryhttp://dx.doi.org/10.1101/2024.06.25.600585

repositoryhttps://pubmed.ncbi.nlm.nih.gov/38979226

Fields of Study

Epigenetics and DNA MethylationCancer-related molecular mechanisms researchCancer Genomics and Diagnostics

Keywords

EpigeneticsBiologyReprogrammingCTCFTranscriptomeCancer stem cellGene expressionEpigenomicsCancer researchCancer cellStem cellDNA methylationCancerComputational biologyGeneGeneticsEnhancerMachine LearningHistonesGene Expression Regulation, NeoplasticBrain NeoplasmsGene Expression ProfilingNeoplastic Stem CellsHumansGlioblastomaArticleResearch ArticleEpigenesis, Geneticcross-patient predictiondeep learningtranscriptomics

Sustainable Development Goals

SDG 0: Good health and well-being