Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Using Sequence Motifs for Enhanced Neural Network Prediction of Protein Distance Constraints
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
PROFcon: novel prediction of long-range contacts
Bioinformatics
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
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Proteins are one of the most important molecules in organisms. Protein function can be inferred from its 3D structure. The gap between the number of discovered protein sequences and the number of structures determined by the experimental methods is increasing. Accurate prediction of protein contact map is an important step toward the reconstruction of the protein's 3D structure. In spite of continuous progress in developing contact map predictors, highly accurate prediction is still unresolved problem. In this paper, we introduce a new predictor, JUSTcon, which consists of multiple parallel stages that are based on adaptive neuro-fuzzy inference System (ANFIS) and K nearest neighbors (KNNs) classifier. A smart filtering operation is performed on the final outputs to ensure normal connectivity behaviors of amino acids pairs. The window size of the filter is selected by a simple expert system. The dataset was divided into testing dataset of 50 proteins and training dataset of 450 proteins. The system produced an average accuracy of 45.2% for the sequence separation of six amino acids. In addition, JUSTcon outperformed SVMcon and PROFcon predictors in the cases of large separation distances. JUSTcon produced an average accuracy of 15% for the sequence separation of 24 amino acids after applying it on CASP9 targets.