Classifier ensembles for protein structural class prediction with varying homology
| Autor: | Scott Dick, Kanaka Durga Kedarisetti, Lukasz Kurgan |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Protein structural class
Sequence Homology Amino Acid business.industry Biophysics Computational Biology Pattern recognition Feature selection Ranging Cell Biology Bioinformatics Biochemistry Homology (biology) Sequence homology Predictive Value of Tests Structural Homology Protein Prediction methods Classification methods Artificial intelligence Amino Acid Sequence business Molecular Biology Classifier (UML) Algorithms Mathematics |
| Zdroj: | Biochemical and biophysical research communications. 348(3) |
| ISSN: | 0006-291X |
| Popis: | Structural class characterizes the overall folding type of a protein or its domain. A number of computational methods have been proposed to predict structural class based on primary sequences; however, the accuracy of these methods is strongly affected by sequence homology. This paper proposes, an ensemble classification method and a compact feature-based sequence representation. This method improves prediction accuracy for the four main structural classes compared to competing methods, and provides highly accurate predictions for sequences of widely varying homologies. The experimental evaluation of the proposed method shows superior results across sequences that are characterized by entire homology spectrum, ranging from 25% to 90% homology. The error rates were reduced by over 20% when compared with using individual prediction methods and most commonly used composition vector representation of protein sequences. Comparisons with competing methods on three large benchmark datasets consistently show the superiority of the proposed method. |
| Databáze: | OpenAIRE |
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