biostudies-literatureKeyl PBMBFCharité – Universitätsmedizin BerlinKorea UniversityMSITInstitute of Pathology, MunichKorea governmentIITPBerlin Institute of Health at Charitée20https://www.ebi.ac.uk/biostudies/studies/S-EPMC9976884biostudies-literatureUnknown51(4)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 patteNucleic acids researchSingle-cell gene regulatory network prediction by explainable AI.PMC997688401GQ111501IS14013A-E01IS18025A01GQ08502019-0-0007901IS18037AMSTARS/MSCORESYSBluthgen NMontavon GBockmayr MHorst DBischoff PKlauschen FMuller KRDernbach GKeyl PFritz RfalseSingle-cell gene regulatory network prediction by explainable AI.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 patte2023-01-01T00:00:00Z2023 Feb2026-03-17T15:46:09.455Z2025-04-05T13:02:36.567ZS-EPMC99768843662927410.1093/nar/gkac1212