{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Keyl P"],"funding":["BMBF","Charité – Universitätsmedizin Berlin","Korea University","MSIT","Institute of Pathology, Munich","Korea government","IITP","Berlin Institute of Health at Charité"],"pagination":["e20"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9976884"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["51(4)"],"pubmed_abstract":["The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding of this heterogeneity, it offers a mostly descriptive view on cellular types and states. To obtain more functional insights, we propose scGeneRAI, an explainable deep learning approach that uses layer-wise relevance propagation (LRP) to infer gene regulatory networks from static single-cell RNA sequencing data for individual cells. We benchmark our method with synthetic data and apply it to single-cell RNA sequencing data of a cohort of human lung cancers. From the predicted single-cell networks our approach reveals characteristic network patte"],"journal":["Nucleic acids research"],"pubmed_title":["Single-cell gene regulatory network prediction by explainable AI."],"pmcid":["PMC9976884"],"funding_grant_id":["01GQ1115","01IS14013A-E","01IS18025A","01GQ0850","2019-0-00079","01IS18037A","MSTARS/MSCORESYS"],"pubmed_authors":["Bluthgen N","Montavon G","Bockmayr M","Horst D","Bischoff P","Klauschen F","Muller KR","Dernbach G","Keyl P","Fritz R"],"additional_accession":[]},"is_claimable":false,"name":"Single-cell gene regulatory network prediction by explainable AI.","description":"The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding of this heterogeneity, it offers a mostly descriptive view on cellular types and states. To obtain more functional insights, we propose scGeneRAI, an explainable deep learning approach that uses layer-wise relevance propagation (LRP) to infer gene regulatory networks from static single-cell RNA sequencing data for individual cells. We benchmark our method with synthetic data and apply it to single-cell RNA sequencing data of a cohort of human lung cancers. From the predicted single-cell networks our approach reveals characteristic network patte","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Feb","modification":"2026-03-17T15:46:09.455Z","creation":"2025-04-05T13:02:36.567Z"},"accession":"S-EPMC9976884","cross_references":{"pubmed":["36629274"],"doi":["10.1093/nar/gkac1212"]}}