Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation

Abstract

BackgroundDuring the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue.MethodsWe combine three different networks of drug, genomic and disease phenotype and assign the weights to the edges from available experimental data and knowledge. Given a specific disease, we use our network propagation approach to infer the drug-disease associations.ResultsWe apply prostate cancer and colorectal cancer as our test data. We use the manually curated drug-disease associations from comparative toxicogenomics database to be our benchmark. The ranked results show that our proposed method obtains higher specificity and sensitivity and clearly outperforms previous methods. Our result also show that our method with off-targets information gets higher performance than that with only primary drug targets in both test data.ConclusionsWe clearly demonstrate the feasibility and benefits of using network-based analyses of chemical, genomic and phenotype data to reveal drug-disease associations. The potential associations inferred by our method provide new perspectives for toxicogenomics and drug reposition evaluation.

Integrative methods for analyzing big data in precision medicine

“…For many specific diseases, that data set is unknown, or incomplete, which makes the use of the methods more difficult. To overcome this, Huang et al [143] proposed a completely unsupervised integrative method that can infer drug-disease associations without any prior associations. They used coupled network propagation [161] on drug-drug chemical similarity, diseasedisease phenotype similarity and gene-gene co-expression similarity homogeneous networks, connected by drug-gene and gene-disease heterogeneous networks.…”

“…Coupled network propagation [143] Drug-disease network inference by integrating drug, disease and gene interaction network, as well as drug-gene and gene-disease association network.…”

Methods for biological data integration: perspectives and challenges

“…They simultaneously explore the structure of each network and of their mutual relations; based on all this information, they create an integrated inference. Such approaches, also called network propagation methods, have been applied to biological problems, including genedisease prioritization [69,72], drug-target prediction, drug repurposing [70,71] and drug-disease association prediction [73]. Although these approaches are mainly designed for a pair of inter-related networks, their further extensions to handle more networks are possible.…”

“…Although these approaches are mainly designed for a pair of inter-related networks, their further extensions to handle more networks are possible. For example, Huang et al [73] extended the network propagation method to three interrelated networks. However, with the inclusion of multiple networks, the number of coupled iterative equations for information propagation (diffusion) grows and hence, the running time of the algorithm increases.…”

In silico methods for drug repurposing and pharmacology

“…Network topology can be utilized in graph-based algorithms such as label propagation methods 141 that iteratively propagate information to neighboring nodes; network-based inference 23 methods that make new connections based only on local topology; and shortest-path algorithms to identify parsimonious explanations of network perturbations 142 .…”