Bayesian network multi-classifiers for protein secondary structure prediction

Successful secondary structure predictions provide a starting point for direct tertiary structure modelling, and also can significantly improve sequence analysis and sequence-structure threading for aiding in structure and function determination. Hence the improvement of predictive accuracy of the secondary structure prediction becomes essential for future development of the whole field of protein research. In this work we present several multi-classifiers that combine the predictions of the best current classifiers available on Internet. Our results prove that combining the predictions of a set of classifiers by creating composite classifiers is a fruitful one. We have created multi-classifiers that are more accurate than any of the component classifiers. The multi-classifiers are based on Bayesian networks. They are validated with 9 different datasets. Their predictive accuracy results outperform the best

• Type: Journal
• Year: 2004
• Where: ARTMED
• Authors: Víctor Robles, Pedro Larrañaga, José Manuel Peña, Ernestina Menasalvas Ruiz, María S. Pérez, Vanessa Herves, Anita Wasilewska
• Keywords: ARTMED 2004, Direct Tertiary Structure, Predictions, Predictive Accuracy