Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning | Zendy

Jianzhu Ma | Zendy; Sheng Wang | Zendy; Zhiyong Wang | Zendy; Jinbo Xu | Zendy

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Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning

Author(s) -

Jianzhu Ma,

Sheng Wang,

Zhiyong Wang,

Jinbo Xu

Publication year - 2015

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/btv472

Subject(s) - computer science , artificial intelligence , machine learning , coevolution , lasso (programming language) , set (abstract data type) , constraint (computer aided design) , protein family , supervised learning , graphical model , sequence (biology) , joint (building) , data mining , biology , mathematics , genetics , gene , engineering , artificial neural network , geometry , architectural engineering , paleontology , world wide web , programming language

Protein contact prediction is important for protein structure and functional study. Both evolutionary coupling (EC) analysis and supervised machine learning methods have been developed, making use of different information sources. However, contact prediction is still challenging especially for proteins without a large number of sequence homologs.

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