Network-based prediction of drug–target interactions using an arbitrary-order proximity embedded deep forest | Zendy

Xiangxiang Zeng | Zendy; Siyi Zhu | Zendy; Yuan Hou | Zendy; Pengyue Zhang | Zendy; Lang Li | Zendy; Jing Li | Zendy; Lei Huang | Zendy; Stephen J. Lewis | Zendy; Ruth Nussinov | Zendy; Feixiong Cheng | Zendy

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Network-based prediction of drug–target interactions using an arbitrary-order proximity embedded deep forest

Author(s) -

Xiangxiang Zeng,

Siyi Zhu,

Yuan Hou,

Pengyue Zhang,

Lang Li,

Jing Li,

Lei Huang,

Stephen J. Lewis,

Ruth Nussinov,

Feixiong Cheng

Publication year - 2020

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/btaa010

Subject(s) - chembl , computer science , drugbank , drug repositioning , identification (biology) , modelling biological systems , drug discovery , classifier (uml) , artificial intelligence , machine learning , biological network , snapshot (computer storage) , deep learning , computational biology , data mining , drug , systems biology , bioinformatics , biology , database , pharmacology , botany

Systematic identification of molecular targets among known drugs plays an essential role in drug repurposing and understanding of their unexpected side effects. Computational approaches for prediction of drug-target interactions (DTIs) are highly desired in comparison to traditional experimental assays. Furthermore, recent advances of multiomics technologies and systems biology approaches have generated large-scale heterogeneous, biological networks, which offer unexpected opportunities for network-based identification of new molecular targets among known drugs.

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