Deep mining heterogeneous networks of biomedical linked data to predict novel drug–target associations | Zendy

Nansu Zong | Zendy; Hyeoneui Kim | Zendy; Victoria Ngo | Zendy; Olivier Harismendy | Zendy

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

Author(s) -

Nansu Zong,

Hyeoneui Kim,

Victoria Ngo,

Olivier Harismendy

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx160

Subject(s) - computer science , data mining , drug , computational biology , artificial intelligence , machine learning , medicine , biology , pharmacology

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.

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