Protein complex prediction via cost-based clustering | Zendy

Andrew D. King | Zendy; Nataša Pržulj | Zendy; Igor Jurišica | Zendy

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Protein complex prediction via cost-based clustering

Author(s) -

Andrew D. King,

Nataša Pržulj,

Igor Jurišica

Publication year - 2004

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/bth351

Subject(s) - cluster analysis , computer science , scalability , data mining , partition (number theory) , identification (biology) , biological network , protein function prediction , caenorhabditis elegans , computational biology , protein function , machine learning , biology , mathematics , genetics , botany , combinatorics , database , gene

Understanding principles of cellular organization and function can be enhanced if we detect known and predict still undiscovered protein complexes within the cell's protein-protein interaction (PPI) network. Such predictions may be used as an inexpensive tool to direct biological experiments. The increasing amount of available PPI data necessitates an accurate and scalable approach to protein complex identification.

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