Predicting drug-disease associations by using similarity constrained matrix factorization

• Published in: BMC bioinformatics Vol. 19; no. 1; p. 233
• Main Authors: Zhang, Wen, Yue, Xiang, Lin, Weiran, Wu, Wenjian, Liu, Ruoqi, Huang, Feng, Liu, Feng
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 19-06-2018; BioMed Central; BMC
• Subjects: Alzheimer's disease; Bioinformatics; Case studies; Computation; Computational Biology - methods; Computer applications; Computer simulation; Computer-generated environments; Datasets; Disease; Drug Discovery; Drug interactions; Drug-disease associations; Drugs; Enzymes; Factorization; Genetic algorithms; Humans; Internet; Models, Theoretical; Pharmaceutical Preparations - chemistry; Pharmaceutical Preparations - metabolism; Physiological aspects; Predictions; Research Design; Semantics; Servers; Similarity; Similarity constrained matrix factorization; Teaching methods; China; United States > US; Drug-disease associations; Similarity constrained matrix factorization
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-018-2220-4

Drug-disease associations provide important information for the drug discovery. Wet experiments that identify drug-disease associations are time-consuming and expensive. However, many drug-disease associations are still unobserved or unknown. The development of computational methods for predicting unobserved drug-disease associations is an important and urgent task.
In this paper, we proposed a similarity constrained matrix factorization method for the drug-disease association prediction (SCMFDD), which makes use of known drug-disease associations, drug features and disease semantic information. SCMFDD projects the drug-disease association relationship into two low-rank spaces, which uncover latent features for drugs and diseases, and then introduces drug feature-based similarities and disease semantic similarity as constraints for drugs and diseases in low-rank spaces. Different from the classic matrix factorization technique, SCMFDD takes the biological context of the problem into account. In computational experiments, the proposed method can produce high-accuracy performances on benchmark datasets, and outperform existing state-of-the-art prediction methods when evaluated by five-fold cross validation and independent testing.
We developed a user-friendly web server by using known associations collected from the CTD database, available at http://www.bioinfotech.cn/SCMFDD/ . The case studies show that the server can find out novel associations, which are not included in the CTD database.