Article

Learning to Estimate Sample-specific Transcriptional Networks for 7000 Tumors

2023-12-04

Abstract excerpt

Cancers are shaped by somatic mutations, microenvironment, and patient background, each altering gene expression and regulation in complex ways, resulting in heterogeneous cellular states and dynamics. Inferring gene regulatory networks (GRNs) from expression data can help characterize this regulation-driven heterogeneity, but network inference requires many statistical samples, limiting GRNs to cluster-level anal...

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Literature Corpus work: 1ffc3201-41d1-5c29-85a8-d16710d5d473
DOI: 10.1101/2023.12.01.569658

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