Two-step multi-omics modelling of drug sensitivity in cancer cell lines to identify driving mechanisms.

ABSTRACT: Drug sensitivity prediction models for human cancer cell lines constitute important tools in identifying potential computational biomarkers for responsiveness in a pre-clinical setting. Integrating information derived from a range of heterogeneous data is crucial, but remains non-trivial, as differences in data structures may hinder fitting algorithms from assigning adequate weights to complementary information that is contained in distinct omics data. In order to counteract this effect that tends to lead to just one data type dominating supposedly multi-omics models, we developed a novel tool that enables users to train single-omics models separately in a first step and to integrate them into a multi-omics model in a second step. Extensive ablation studies are performed in order to facili

SUBMITTER: Kusch N 

PROVIDER: S-EPMC7682852 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature