biostudies-literatureUnknownSiahpirani AFNSFNICHD NIH HHSDOENIHNIGMS NIH HHSReconstructing genome-scale gene regulatory networks (GRNs) remains a difficult problem in systems biology, and many experimental and computational methods have been developed to address this problem. Recent computational methods have aimed to more accurately model GRNs by estimating the hidden Transcription Factor Activity (TFA) from prior knowledge of TF target regulatory connections, encoded as an input directed graph, to relax the assumption that mRNA level of the regulator correlates with the protein activity of the regulator. However, the noise in the prior knowledge can adversely affect the estimated TFA levels and the quality of the downstream inferred GRNs. Here, we present a new approach, MERLIN+P+TFA, that uses prior knowledge-guided sparsity regularization to robustly and accurbioRxiv : the preprint server for biology2025.06.09.658650https://www.ebi.ac.uk/biostudies/studies/S-EPMC12259028biostudies-literatureUncovering Functional Gene Regulatory Networks in Bulk and Single-Cell Data through Robust Transcription Factor Activity Estimation and Model-Guided Experimental Validation.PMC12259028R01HD105151R01GM144708DE-SC0021052IOS 1840687R01 GM117339R01GM117339IOS 24066533R01 GM144708R01 HD105151Dillingham CPyne SMcCalla SGRoy SSiahpirani AFSridharan RfalseUncovering Functional Gene Regulatory Networks in Bulk and Single-Cell Data through Robust Transcription Factor Activity Estimation and Model-Guided Experimental Validation.Reconstructing genome-scale gene regulatory networks (GRNs) remains a difficult problem in systems biology, and many experimental and computational methods have been developed to address this problem. Recent computational methods have aimed to more accurately model GRNs by estimating the hidden Transcription Factor Activity (TFA) from prior knowledge of TF target regulatory connections, encoded as an input directed graph, to relax the assumption that mRNA level of the regulator correlates with the protein activity of the regulator. However, the noise in the prior knowledge can adversely affect the estimated TFA levels and the quality of the downstream inferred GRNs. Here, we present a new approach, MERLIN+P+TFA, that uses prior knowledge-guided sparsity regularization to robustly and accur2025-01-01T00:00:00Z2025 Jul2026-04-26T03:22:46.578Z2026-04-26T03:12:01.932ZS-EPMC122590284066160110.1101/2025.06.09.658650