The utility of different representations of protein sequence for predicting functional class | Zendy

Ross D. King | Zendy; Andreas Karwath | Zendy; Amanda Clare | Zendy; Luc De Raedt | Zendy

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The utility of different representations of protein sequence for predicting functional class

Author(s) -

Ross D. King,

Andreas Karwath,

Amanda Clare,

Luc De Raedt

Publication year - 2001

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/17.5.445

Subject(s) - orfs , sequence (biology) , representation (politics) , function (biology) , computational biology , class (philosophy) , computer science , protein sequencing , biology , genetics , artificial intelligence , data mining , peptide sequence , open reading frame , gene , politics , political science , law

Data Mining Prediction (DMP) is a novel approach to predicting protein functional class from sequence. DMP works even in the absence of a homologous protein of known function. We investigate the utility of different ways of representing protein sequence in DMP (residue frequencies, phylogeny, predicted structure) using the Escherichia coli genome as a model.

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