A Flexible and Robust Multi-Source Learning Algorithm for Drug Repositioning

Drug repositioning is a promising strategy in drug discovery. New biomedical insights of drug-target-disease relationships are important in drug repositioning, and such relationships have been intensively studied recently. Most of the studies utilize network-based computational approaches based on drug and disease similarities. However, one common limitation of existing approaches is that both drug similarities and disease similarities are defined based on a single feature of drugs/diseases. In reality, the relationships between drug (or disease) pairs can be characterized based on many different features. Therefore, it is increasingly important to include them in drug repositioning studies. In this study, we propose a flexible and robust multi-source learning (FRMSL) framework to integrate multiple heterogeneous data sources for drug-disease association predictions. We first construct a two-layer heterogeneous network consisting of drug nodes, disease nodes and known drug-disease relationships. The drug repositioning problem can thus be treated as a missing link prediction problem on the heterogeneous graph and can be solved using Kronecker regularized least square (KronRLS) method. Multiple data sources describing drugs and diseases are incorporated into the framework using similarity-based kernels. In practice, a great challenge in such data integration projects is the data incompleteness problem due to the nature of data generation and collection. To address this issue, we develop a novel multi-view learning algorithm based on symmetric nonnegative matrix factorization (SymNMF). Extensive experimental studies show that our framework outperforms several recent network-based methods.