A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.

ABSTRACT: Cancers that appear pathologically similar often respond differently to the same drug regimens. Methods to better match patients to drugs are in high demand. We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia (AML) by introducing: data from 30 AML patients including genome-wide gene expression profiles and in vitro sensitivity to 160 chemotherapy drugs, a computational method to identify reliable gene expression markers for drug sensitivity by incorporating multi-omic prior information relevant to each gene's potential to drive cancer. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately. Finally,

SUBMITTER: Lee SI 

PROVIDER: S-EPMC5752671 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature