Article

Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples

2020-10-07

Abstract excerpt

Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to...

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Literature Corpus work: fa2a5599-6fae-57f0-90e2-170759766c32
DOI: 10.1101/2020.10.07.329417

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