  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.
