Revista: | Computación y sistemas |
Base de datos: | |
Número de sistema: | 000560381 |
ISSN: | 1405-5546 |
Autores: | Quintana Zaez, Julio C.1 Molina Ruiz, Reinaldo4 Santiesteban Toca, Cosme E.3 |
Instituciones: | 1University of Ciego de Ávila “Máximo Gómez Báez”, Faculty of Informatics, Cuba 2Universidad Central Marta Abreu de las Villas, Informatics Research Center, Santa Clara, Villa Clara. Cuba 3University of Ciego de Ávila “Máximo Gómez Báez”, Bioplantas Research Center, Cuba 4Universidad Central Marta Abreu de las Villas, Center of Chemical Reagents, Santa Clara, Villa Clara. Cuba |
Año: | 2018 |
Periodo: | Oct-Dic |
Volumen: | 22 |
Número: | 4 |
Paginación: | 1559-1571 |
País: | México |
Idioma: | Inglés |
Resumen en inglés | Understanding the folding of proteins is one of the most interesting research field for the Bioinformatics. The contact maps constitute an intermediate step in the prediction of the 3D structure of the proteins and allow to represent folding patterns. Currently, the methods used to predict contact maps achieve low precision results, only about 25% of long-range (L/5) contacts are correctly predicted, and their knowledge base is not humanly interpretable. In this paper, we propose an easy implementation multiple classifier for contact maps, which is based on patterns of interaction between secondary structures and employed decision trees as base classifiers. This method is able to naturally reduce the level of imbalance between contact/non-contact classes. In addition, a set of interpretable rules are extracted as a complement to the prediction. The validation of method performance shows that an average of 45% of general contacts are correctly predicted. Moreover, a Z-score comparison of its long-range contacts predictions (L/5) with participant methods in CASP11 competition shows that it is competitive with the state-of-the-art methods. |
Keyword: | Contact maps, Folding patterns, Decision trees, Long-range contacts |
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