Predicting drug-disease associations by using similarity constrained matrix factorization

TLDR

Drug‑disease associations are crucial for drug discovery, yet many remain unobserved and identifying them experimentally is time‑consuming and costly. The study aims to develop computational methods to predict unobserved drug‑disease associations. SCMFDD projects drug‑disease relationships into two low‑rank spaces, incorporating drug feature–based and disease semantic similarities as constraints to capture biological context, and is implemented in a user‑friendly web server. Computational experiments show that SCMFDD achieves higher accuracy than existing state‑of‑the‑art methods on benchmark datasets, and case studies demonstrate its ability to uncover novel drug‑disease associations.

Abstract

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.