Inferring drug-disease associations based on known protein complexes

Abstract

Inferring drug-disease associations is critical in unveiling disease mechanisms, as well as discovering novel functions of available drugs, or drug repositioning. Previous work is primarily based on drug-gene-disease relationship, which throws away many important information since genes execute their functions through interacting others. To overcome this issue, we propose a novel methodology that discover the drug-disease association based on protein complexes. Firstly, the integrated heterogeneous network consisting of drugs, protein complexes, and disease are constructed, where we assign weights to the drug-disease association by using probability. Then, from the tripartite network, we get the indirect weighted relationships between drugs and diseases. The larger the weight, the higher the reliability of the correlation. We apply our method to mental disorders and hypertension, and validate the result by using comparative toxicogenomics database. Our ranked results can be directly reinforced by existing biomedical literature, suggesting that our proposed method obtains higher specificity and sensitivity. The proposed method offers new insight into drug-disease discovery. Our method is publicly available at http://1.complexdrug.sinaapp.com/Drug_Complex_Disease/Data_Download.html.

DermaGene and VitmiRS: a comprehensive systems analysis of genetic dermatological disorders

“…Several unrelated diseases have been revealed to show common molecular mechanisms with strong association among them (Yu et al 2015). As a result of such disease associations, the possibility of related diseases to occur together in an individual cannot be ruled out.…”

“…Recent research has growingly demonstrated that many of the seemingly disparate diseases have a common molecular mechanism and strong association among them. This signifies the deeper interplay of genes, in contrast to one gene-one disease commonality well indicating towards the possibility of the related diseases to occur together in an individual (Yu et al 2015). The exhibition of comorbidity association suggests that the occurrence of one disease will increase the likelihood of the other thereby contributing to compounded healthcare costs and multiplex clinical management.…”

Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks

“…Chen et al formulated the drug-disease association prediction problem as recommending preferable diseases for drugs so that two existing recommendation methods, ProbS and HeatS, were used to infer drug-disease associations [4]. Yu et al used protein complexes as an intermediate bridge to construct a tripartite network consisting of drugs, protein complexes, and disease, on which the likelihood probabilities of drug-disease associations were inferred [16]. Luo et al exploited known drug-disease associations to improve the drug-drug and disease-disease similarity measures, and then integrated the similarity networks and drug-disease associations to build a drug-disease heterogenous network, on which a bi-random walk algorithm is proposed to predict novel potential drug-disease associations [17].…”

Protein complex prediction for large protein protein interaction networks with the Core&Peel method

“…However, extracting useful knowledge from such networks is not straightforward. Therefore sophisticated PPI network analysis algorithms have been devised in the last decade for several goals such as: the prediction of protein-complexes ([1]), the prediction of higher level functional modules ([2–4]), the prediction of unknown interactions ([5, 6]), the prediction of single protein functions ([7]), the elucidation of the molecular basis of diseases ([8]), and the discovery of drug-disease associations ([9]), to name just a few. In this paper we concentrate on the issue of predicting protein-complexes (PC) in PPI networks.…”