A hybrid wavelet-based fingerprint matcher
Pattern Recognition
Generalized Needleman-Wunsch algorithm for the recognition of T-cell epitopes
Expert Systems with Applications: An International Journal
Letters: Adaptive local hyperplane classification
Neurocomputing
An ensemble of support vector machines for predicting virulent proteins
Expert Systems with Applications: An International Journal
Coding of amino acids by texture descriptors
Artificial Intelligence in Medicine
Prediction of protein protein interactions from primary sequences
International Journal of Data Mining and Bioinformatics
Reduced Reward-punishment editing for building ensembles of classifiers
Expert Systems with Applications: An International Journal
Prediction of protein-protein interactions using subcellular and functional localizations
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
TC-VGC: A Tumor Classification System using Variations in Genes' Correlation
Computer Methods and Programs in Biomedicine
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A novel method for prediction of protein interaction sites based on integrated RBF neural networks
Computers in Biology and Medicine
Perceptual relativity-based local hyperplane classification
Neurocomputing
Mining from protein–protein interactions
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Domain information based prediction of protein-protein interactions of glucosinolate biosynthesis
International Journal of Computer Applications in Technology
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Prediction of protein--protein interaction is a difficult and important problem in biology. In this paper, we propose a new method based on an ensemble of K-local hyperplane distance nearest neighbor (HKNN) classifiers, where each HKNN is trained using a different physicochemical property of the amino acids. Moreover, we propose a new encoding technique that combines the amino acid indices together with the 2-Grams amino acid composition. A fusion of HKNN classifiers combined with the 'Sum rule' enables us to obtain an improvement over other state-of-the-art methods. The approach is demonstrated by building a learning system based on experimentally validated protein--protein interactions in human gastric bacterium Helicobacter pylori and in Human dataset. Contact: lnanni@deis.unibo.it