MOCASSIN-prot: a multi-objective clustering approach for protein similarity networks
Author(s) -
Brittney N. Keel,
Bo Deng,
Etsuko N. Moriyama
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/btx755
Subject(s) - cluster analysis , computer science , uniprot , shuffling , protein sequencing , data mining , protein domain , similarity (geometry) , domain (mathematical analysis) , hierarchical clustering , computational biology , spectral clustering , artificial intelligence , peptide sequence , biology , mathematics , genetics , gene , mathematical analysis , image (mathematics) , programming language
Proteins often include multiple conserved domains. Various evolutionary events including duplication and loss of domains, domain shuffling, as well as sequence divergence contribute to generating complexities in protein structures, and consequently, in their functions. The evolutionary history of proteins is hence best modeled through networks that incorporate information both from the sequence divergence and the domain content. Here, a game-theoretic approach proposed for protein network construction is adapted into the framework of multi-objective optimization, and extended to incorporate clustering refinement procedure.
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