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Predicting protein-protein interaction from primary structure support vector machines

Alashwal, Hany Taher (2004) Predicting protein-protein interaction from primary structure support vector machines. In: International Conference on Bioinformatics , 2004, Auckland.

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Abstract

Motivation: An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. The expectation is that this will provide a fuller appreciation of cellular processes and networks at the protein level, ultimately leading to a better understanding of disease mechanisms and suggesting new means for intervention. This paper addresses the question: can protein–protein interactions be predicted directly from primary structure and associated data? Using a diverse database of known protein interactions, a Support Vector Machine (SVM) learning system was trained to recognize and predict interactions based solely on primary structure and associated physicochemical properties. Results: Inductive accuracy of the trained system, defined here as the percentage of correct protein interaction predictions for previously unseen test sets, averaged 80% for the ensemble of statistical experiments. Future proteomics studies may benefit from this research by proceeding directly from the automated identification of a cell’s gene products to prediction of protein interaction pairs.

Item Type:Conference or Workshop Item (Paper)
Subjects:Unspecified
Divisions:Others
ID Code:20661
Deposited By: Liza Porijo
Last Modified:28 Dec 2011 15:55

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