Evaluation of single-sample network inference methods for precision oncology.

ABSTRACT: A major challenge in precision oncology is to detect targetable cancer vulnerabilities in individual patients. Modeling high-throughput omics data in biological networks allows identifying key molecules and processes of tumorigenesis. Traditionally, network inference methods rely on many samples to contain sufficient information for learning, resulting in aggregate networks. However, to implement patient-tailored approaches in precision oncology, we need to interpret omics data at the level of individual patients. Several single-sample network inference methods have been developed that infer biological networks for an individual sample from bulk RNA-seq data. However, only a limited comparison of these methods has been made and many methods rely on 'normal tissue' samples as reference, whi

SUBMITTER: Deschildre J 

PROVIDER: S-EPMC10869342 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature