Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties.

ABSTRACT: A key goal of precision medicine is predicting the best drug therapy for a specific patient from genomic information. In oncology, cancers that appear similar pathologically can vary greatly in how they respond to the same drug. Fortunately, data from high-throughput screening programs often reveal important relationships between genomic variability of cancer cells and their response to drugs. Nevertheless, many current computational methods to predict compound activity against cancer cells require large quantities of genomic, epigenomic, and additional cellular data to develop and to apply. Here we integrate recent screening data and machine learning to train classification models that predict the activity/inactivity of compounds against cancer cells based on the mutational status of only

SUBMITTER: Lind AP 

PROVIDER: S-EPMC6622537 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature