Gaussian interaction profile kernels for predicting drug–target interaction | Zendy

Twan van Laarhoven | Zendy; Sander B. Nabuurs | Zendy; Elena Marchiori | Zendy

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Gaussian interaction profile kernels for predicting drug–target interaction

Author(s) -

Twan van Laarhoven,

Sander B. Nabuurs,

Elena Marchiori

Publication year - 2011

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/btr500

Subject(s) - computer science , interaction network , drug target , kernel (algebra) , gaussian , classifier (uml) , machine learning , artificial intelligence , drug drug interaction , precision and recall , simple (philosophy) , binary number , binary classification , data mining , drug , support vector machine , mathematics , biology , biochemistry , philosophy , physics , epistemology , combinatorics , quantum mechanics , gene , pharmacology , arithmetic

The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of all drug-target pairs in current datasets are experimentally validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy.

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