Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients.

ABSTRACT: Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among

SUBMITTER: Guo WF 

PROVIDER: S-EPMC8053130 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature