Machine learning prediction of oncology drug targets based on protein and network properties.

ABSTRACT: BACKGROUND:The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, reducing the cost and the time needed. RESULTS:We developed a machine learning approach to score proteins to generate a druggability score of novel targets. In our model we incorporated 70 protein features which included properties derived from the sequence, features characterizing protein functions as well as network properties derived from the protein-protein interaction network. The advantage of this approach is that it is unbiased and even less studied proteins with limited information about their function can score well as most of the featur

SUBMITTER: Dezso Z 

PROVIDER: S-EPMC7071582 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature