Abstract
Identifying drug-disease associations is integral to drug development. Computationally prioritizing candidate drug-disease associations has attracted growing attention due to its contribution to reducing the cost of laboratory screening. Drug-disease associations involve different association types, such as drug indications and drug side effects. However, the existing models for predicting drug-disease associations merely concentrate on independent tasks: recommending novel indications to benefit drug repositioning, predicting potential side effects to prevent drug-induced risk, or only determining the existence of drug-disease association. They ignore crucial prior knowledge of the correlations between different association types. Since the Comparative Toxicogenomics Database (CTD) annotates the drug-disease associations as therapeutic or marker/mechanism, we consider predicting the two types of association. To this end, we propose a collective matrix factorization-based multi-task learning method (CMFMTL) in this paper. CMFMTL handles the problem as multi-task learning where each task is to predict one type of association, and two tasks complement and improve each other by capturing the relatedness between them. First, drug-disease associations are represented as a bipartite network with two types of links representing therapeutic effects and non-therapeutic effects. Then, CMFMTL, respectively, approximates the association matrix regarding each link type by matrix tri-factorization, and shares the low-dimensional latent representations for drugs and diseases in the two related tasks for the goal of collective learning. Finally, CMFMTL puts the two tasks into a unified framework and an efficient algorithm is developed to solve our proposed optimization problem. In the computational experiments, CMFMTL outperforms several state-of-the-art methods both in the two tasks. Moreover, case studies show that CMFMTL helps to find out novel drug-disease associations that are not included in CTD, and simultaneously predicts their association types.
Highlights
Drugs are chemicals used to treat, cure, prevent, or diagnose diseases
We propose a collective matrix factorizationbased multi-task learning method to predict two types of drug-disease associations
Case studies show that collective matrix factorization-based multi-task learning method (CMFMTL) helps to find out novel drug-disease associations that are not included in Comparative Toxicogenomics Database (CTD), and simultaneously predicts their association types
Summary
Drugs are chemicals used to treat, cure, prevent, or diagnose diseases. The development of a new drug has three steps: discovery stage, preclinical stage, and clinical stage (Wilson, 2006), which takes about 15 years (Dimasi, 2001) and costs about 1,000 million U.S dollars (Adams and Brantner, 2006). Traditional wet-lab experiments are expensive and laborious In light of these challenges, computational methods which associate drugs with diseases have attracted growing attention from the biomedical community. A large number of computational methods have been proposed for the drug-disease association prediction. Zhang et al (2017b) presented a novel bipartite network-based method, which only used known drug-disease associations to predict unobserved associations. We propose a collective matrix factorizationbased multi-task learning method (abbreviated as “CMFMTL”) to predict two types of drug-disease associations. CMFMTL, respectively, approximates the association matrix regarding each link type by matrix tri-factorization, and shares the low-dimensional latent representations for drugs and diseases in the two related tasks for the goal of collective learning. Case studies show that CMFMTL helps to find out novel drug-disease associations that are not included in CTD, and simultaneously predicts their association types
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