Automatic prediction of protein domains from sequence information using a hybrid learning system | Zendy

Niranjan Nagarajan | Zendy; Golan Yona | Zendy

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Automatic prediction of protein domains from sequence information using a hybrid learning system

Author(s) -

Niranjan Nagarajan,

Golan Yona

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/bth086

Subject(s) - computer science , sequence (biology) , protein sequencing , artificial intelligence , machine learning , data mining , peptide sequence , biology , gene , genetics

We describe a novel method for detecting the domain structure of a protein from sequence information alone. The method is based on analyzing multiple sequence alignments that are derived from a database search. Multiple measures are defined to quantify the domain information content of each position along the sequence and are combined into a single predictor using a neural network. The output is further smoothed and post-processed using a probabilistic model to predict the most likely transition positions between domains.

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