A survey of multiple classifier systems as hybrid systems <s> Proteomics <s> We have developed a hybrid system to predict the secondary structures (α-helix, β-sheet and coil) of proteins and achieved 66.4% accuracy, with correlation coefficients of Ccoil = 0.429, Cα = 0.470 and Cβ = 0.387. This system contains three subsystems (“experts”): a neural network module, a statistical module and a memory-based reasoning module. First, the three experts independently learn the mapping between amino acid sequences and secondary structures from the known protein structures, then a Combiner learns to combine automatically the outputs of the experts to make final predictions. The hybrid system was tested with 107 protein structures through k-way cross-validation. Its performance was better than each expert and all previously reported methods with greater than 0.99 statistical significance. It was observed that for 20% of the residues, all three experts produced the same but wrong predictions. This may suggest an upper bound on the accuracy of secondary structure predictions based on local information from the currently available protein structures, and indicate places where non-local interactions may play a dominant role in conformation. For 64% of the residues, at least two experts were the same and correct, which shows that the Combiner performed better than majority vote. For 77 % of the residues, at least one expert was correct, thus there may still be room for improvement in this hybrid approach. Rigorous evaluation procedures were used in testing the hybrid system, and statistical significance measures were developed in analyzing the differences among different methods. When measured in terms of the number of secondary structures (rather than the number of residues) that were predicted correctly, the prediction produced by the hybrid system was also better than those of individual experts. <s> BIB001 </s> A survey of multiple classifier systems as hybrid systems <s> Proteomics <s> Predicting the three-dimensional structure of a protein from its amino-acid sequence is an important problem in bioinformatics and a challenging task for machine learning algorithms. In this paper, we investigate an ensemble of classifiers, applied for protein fold recognition, and tested it on a real-world dataset. The obtained results are very encouraging, our results improved the average predictive accuracy obtained in previously published work. <s> BIB002
Proteins are said to have a common fold if they have the same major secondary structure in the same arrangement and with the same topology. Machine learning techniques have been proposed for three-dimensional protein structure prediction. Early approaches consisted in hybrid systems, such as the ANN, statistical classifier and case base reasoning classifier combined by majority voting of BIB001 . For instance, an ensemble of K-local hyperplanes based on random subspace and feature selection has been tested BIB002 , where feature selection is done according to distance to the class centroids. A recent approach is the MarFold  combining by majority voting three margin-based classifiers for protein fold recognition: the adaptive local hyperplane (ALH), the k-neighborhood ALH and the SVM.