Kernel methods for predicting protein-protein interactions | Zendy

Asa BenHur | Zendy; William Stafford Noble | Zendy

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Kernel methods for predicting protein-protein interactions

Author(s) -

Asa BenHur,

William Stafford Noble

Publication year - 2005

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/bti1016

Subject(s) - computer science , pairwise comparison , false positive paradox , kernel (algebra) , kernel method , classifier (uml) , protein–protein interaction , artificial intelligence , machine learning , protein sequencing , support vector machine , data mining , pattern recognition (psychology) , computational biology , peptide sequence , biology , mathematics , gene , genetics , combinatorics

Despite advances in high-throughput methods for discovering protein-protein interactions, the interaction networks of even well-studied model organisms are sketchy at best, highlighting the continued need for computational methods to help direct experimentalists in the search for novel interactions.

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