Comparison of multiple modalities for drug response prediction with learning curves using neural networks and XGBoost.

ABSTRACT:

Motivation

Anti-cancer drug response prediction is a central problem within stratified medicine. Transcriptomic profiles of cancer cell lines are typically used for drug response prediction, but we hypothesize that proteomics or phosphoproteomics might be more suitable as they give a more direct insight into cellular processes. However, there has not yet been a systematic comparison between all three of these datatypes using consistent evaluation criteria.

Results

Due to the limited number of cell lines with phosphoproteomics profiles we use learning curves, a plot of predictive performance as a function of dataset size, to compare the current performance and predict the future performance of the three omics datasets with more data. We use neural networks and XGBoost and com

SUBMITTER: Branson N 

PROVIDER: S-EPMC10812874 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature