Abstract-The classification of protein structures is essential for their function determination in bioinformatics. At present, a reasonably high rate of prediction accuracy has been achieved in classifying proteins into four classes in the SCOP database according to their primary amino acid sequences. However, for further classification into fine-grained folding categories, especially when the number of possible folding patterns as those defined in the SCOP database is large, it is still quite a challenge. In our previous work, we have proposed a two-level classification strategy called hierarchical learning architecture (HLA) using neural networks and two indirect coding features to differentiate proteins according to their classes and folding patterns, which achieved an accuracy rate of 65.5%. In this paper, we use a combinatorial fusion technique to facilitate feature selection and combination for improving predictive accuracy in protein structure classification. When applying various criteria in combinatorial fusion to the protein fold prediction approach using neural networks with HLA and the radial basis function network (RBFN), the resulting classification has an overall prediction accuracy rate of 87% for four classes and 69.6% for 27 folding categories. These rates are significantly higher than the accuracy rate of 56.5% previously obtained by Ding and Dubchak. Our results demonstrate that data fusion is a viable method for feature selection and combination in the prediction and classification of protein structure. Index Terms-Combinatorial fusion analysis (CFA), data fusion, diversity rank/score graph, hierarchical learning architecture (HLA), neural network (NN), protein structure prediction, radical basis function network (RBFN), rank/score functions. Manuscript
REF suggested a Hierarchical Learning Architecture (HLA) using NNs and some indirect coding features.
Feature Selection and Combination Criteria for Improving Accuracy in Protein Structure Prediction
IEEE Transactions on NanoBioscience
IEEE Transactions on NanoBioscience
Computer Science Medicine