An ensemble of K-local hyperplanes for predicting protein-protein interactions.
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.
DEIS, IEIIT, CNR, Università di Bologna Viale Risorgimento 2, 40136 Bologna, Italy. lnanni@deis.unibo.it
Loris
L
Nanni
Alessandra
A
Lumini
D000465:Algorithms; D000595:Amino Acid Sequence; D001665:Binding Sites; D003198:Computer Simulation; D008954:Models, Biological; D008956:Models, Chemical; D008969:Molecular Sequence Data; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*