Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting.

ABSTRACT: Drug response prediction (DRP) methods tackle the complex task of associating the effectiveness of small molecules with the specific genetic makeup of the patient. Anti-cancer DRP is a particularly challenging task requiring costly experiments as underlying pathogenic mechanisms are broad and associated with multiple genomic pathways. The scientific community has exerted significant efforts to generate public drug screening datasets, giving a path to various machine learning models that attempt to reason over complex data space of small compounds and biological characteristics of tumors. However, the data depth is still lacking compared to application domains like computer vision or natural language processing domains, limiting current learning capabilities. To combat this issue and improv

SUBMITTER: Narykov O 

PROVIDER: S-EPMC11966611 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature