Cancer gene identification through integrating causal prompting large language model with omics data-driven causal inference.

ABSTRACT: Identifying genes causally linked to cancer from a multi-omics perspective is essential for understanding the mechanisms of cancer and improving therapeutic strategies. Traditional statistical and machine-learning methods that rely on generalized correlation approaches to identify cancer genes often produce redundant, biased predictions with limited interpretability, largely due to overlooking confounding factors, selection biases, and the nonlinear activation function in neural networks. In this study, we introduce a novel framework for identifying cancer genes across multiple omics domains, named ICGI (Integrative Causal Gene Identification), which leverages a large language model (LLM) prompted with causality contextual cues and prompts, in conjunction with data-driven causal feature se

SUBMITTER: Zeng H 

PROVIDER: S-EPMC11899576 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature