DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction

TL;DR

DIVERSE is a Bayesian framework that systematically integrates multi-omics data to improve drug response prediction, especially in challenging out-of-matrix scenarios, advancing precision medicine efforts.

Contribution

It introduces a novel Bayesian importance-weighted matrix factorization method that sequentially combines multiple heterogeneous data sources for drug response prediction.

Findings

Outperforms five existing methods in cross-validation

Shows significant improvement in out-of-matrix prediction

Successfully identifies potential new drug candidates

Abstract

Detecting predictive biomarkers from multi-omics data is important for precision medicine, to improve diagnostics of complex diseases and for better treatments. This needs substantial experimental efforts that are made difficult by the heterogeneity of cell lines and huge cost. An effective solution is to build a computational model over the diverse omics data, including genomic, molecular, and environmental information. However, choosing informative and reliable data sources from among the different types of data is a challenging problem. We propose DIVERSE, a framework of Bayesian importance-weighted tri- and bi-matrix factorization(DIVERSE3 or DIVERSE2) to predict drug responses from data of cell lines, drugs, and gene interactions. DIVERSE integrates the data sources systematically, in a step-wise manner, examining the importance of each added data set in turn. More specifically, we sequentially integrate five different data sets, which have not all been combined in earlier bioinformatic methods for predicting drug responses. Empirical experiments show that DIVERSE clearly outperformed five other methods including three state-of-the-art approaches, under cross-validation, particularly in out-of-matrix prediction, which is closer to the setting of real use cases and more challenging than simpler in-matrix prediction. Additionally, case studies for discovering new drugs further confirmed the performance advantage of DIVERSE.