Integrative Neural Network Approach for Protein Interaction Prediction from Heterogeneous Data

Authors:
Xue-Wen Chen;Mei Liu;Yong Hu
Affiliations:
Bioinformatics and Computational Life-Sciences Laboratory, ITTC, Department of Electrical Engineering and Computer Science, The University of Kansas, Lawrence, USA KS 66045;Bioinformatics and Computational Life-Sciences Laboratory, ITTC, Department of Electrical Engineering and Computer Science, The University of Kansas, Lawrence, USA KS 66045;Department of Management Science, School of Business Management, Sun Yat-sen University, Guangdong University of Foreign Studies, Guangzhou, Guangdong, China 510275
Venue:
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Year:
2008

Citing 2
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Prediction of protein--protein interactions using random decision forest framework

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Domain-based predictive models for protein-protein interaction prediction

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Abstract

Protein interactions are essential in discovery of protein functions and fundamental biological processes. In this paper, we aim to create a reliable computational model for protein interaction prediction by integrating information from complementary data sources. An integrative Artificial Neural Network (ANN) framework is developed to predict protein-protein interactions (PPIs) from heterogeneous data in Human. Performance of our proposed framework is empirically investigated by combining protein domain data, molecular function and biological process annotations in Gene Ontology. Experimental results demonstrate that our approach can predict PPIs with high sensitivity of 82.43% and specificity of 78.67%. The results suggest that combining multiple data sources can result in a 7% increase in sensitivity compared to using only domain information. We are able to construct a protein interaction network with proteins around mitotic spindle checkpoint of the human interactome map. Novel predictions are made and some are supported by evidences in literature.