Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations.

ABSTRACT:

Motivation

A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction.

Results

We propose a similarity-based drug-target prediction method that enhances existing association discovery methods by using a topology-based similarity measure. DeepWalk, a deep learning method, is adopted in this study to calculate the similarities within Linked Tripartite Network (LTN), a heterogeneous network generated from b

SUBMITTER: Zong N 

PROVIDER: S-EPMC5860112 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature