Large-scale Protein-Protein Interaction prediction using novel kernel methods

Authors:
Xue-wen Chen;Bing Han;Jianwen Fang;Ryan J. Haasl
Affiliations:
Electrical Engineering and Computer Science Department, The University of Kansas, Lawrence, KS 66045, USA.;Electrical Engineering and Computer Science Department, The University of Kansas, Lawrence, KS 66045, USA.;Bioinformatics Core Facility, University of Kansas, Lawrence, KS 66047, USA.;Bioinformatics Core Facility, University of Kansas, Lawrence, KS 66047, USA
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2008

Citing 10
Cited 3

Fast training of support vector machines using sequential minimal optimization

Advances in kernel methods
AI Game Programming Wisdom

AI Game Programming Wisdom
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Support Vector Machines: Training and Applications

Support Vector Machines: Training and Applications
Predicting protein--protein interaction by searching evolutionary tree automorphism space

Bioinformatics
Kernel methods for predicting protein--protein interactions

Bioinformatics
Predicting protein--protein interactions using signature products

Bioinformatics
Prediction of protein–protein interactions by combining structure and sequence conservation in protein interfaces

Bioinformatics
Prediction of protein--protein interactions using random decision forest framework

Bioinformatics
Large-scale Protein-Protein Interaction prediction using novel kernel methods

International Journal of Data Mining and Bioinformatics

Large-scale Protein-Protein Interaction prediction using novel kernel methods

International Journal of Data Mining and Bioinformatics
MentalSquares: A generic bipolar Support Vector Machine for psychiatric disorder classification, diagnostic analysis and neurobiological data mining

International Journal of Data Mining and Bioinformatics
PIMiner: a web tool for extraction of protein interactions from biomedical literature

International Journal of Data Mining and Bioinformatics

Quantified Score

Hi-index	0.01

Visualization

Abstract

Knowledge of Protein-Protein Interactions (PPIs) can give us new insights into molecular mechanisms and properties of the cell. In this paper, we propose a novel domain-based kernel method to predict PPIs. A new kernel that measures the similarity between protein pairs based on a new feature representation is developed and applied to a large scale PPI database. Experimental results demonstrate its effectiveness. Furthermore, we evaluate the problem of cross-species PPI prediction and the effect of the number of negative samples on the performance of PPI predictions, which are two fundamental problems in most in silico PPI methods.