Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers.

ABSTRACT: Cancer is a disease of gene dysregulation, where cells acquire somatic and epigenetic alterations that drive aberrant cellular signaling. These alterations adversely impact transcriptional programs and cause profound changes in gene expression. Interpreting somatic alterations within context-specific transcriptional programs will facilitate personalized therapeutic decisions but is a monumental task. Toward this goal, we develop a partially interpretable neural network model called Chromatin-informed Inference of Transcriptional Regulators Using Self-attention mechanism (CITRUS). CITRUS models the impact of somatic alterations on transcription factors and downstream transcriptional programs. Our approach employs a self-attention mechanism to model the contextual impact of somatic alteratio

SUBMITTER: Tao Y 

PROVIDER: S-EPMC9638905 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature