Predicting allergenic proteins using wavelet transform
Author(s) -
Kuo-Bin Li,
Praveen Issac,
Arun V. Krishnan
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/bth286
Subject(s) - hidden markov model , computer science , sequence motif , protein sequencing , cluster analysis , computational biology , similarity (geometry) , pattern recognition (psychology) , sequence alignment , motif (music) , multiple sequence alignment , allergen , markov chain , artificial intelligence , data mining , biology , machine learning , genetics , peptide sequence , dna , gene , physics , acoustics , image (mathematics) , allergy , immunology
With many transgenic proteins introduced today, the ability to predict their potential allergenicity has become an important issue. Previous studies were based on either sequence similarity or the protein motifs identified from known allergen databases. The similarity-based approaches, although being able to produce high recalls, usually have low prediction precisions. Previous motif-based approaches have been shown to be able to improve the precisions on cross-validation experiments. In this study, a system that combines the advantages of similarity-based and motif-based prediction is described.
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