Prediction of response to anti-cancer drugs becomes robust via network integration of molecular data.

ABSTRACT: Despite the widening range of high-throughput platforms and exponential growth of generated data volume, the validation of biomarkers discovered from large-scale data remains a challenging field. In order to tackle cancer heterogeneity and comply with the data dimensionality, a number of network and pathway approaches were invented but rarely systematically applied to this task. We propose a new method, called NEAmarker, for finding sensitive and robust biomarkers at the pathway level. scores from network enrichment analysis transform the original space of altered genes into a lower-dimensional space of pathways. These dimensions are then correlated with phenotype variables. The method was first tested using in vitro data from three anti-cancer drug screens and then on clinical data of The

SUBMITTER: Franco M 

PROVIDER: S-EPMC6382934 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature