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Predicting Protein-Disease Relationships Using Sequence, Physicochemical Properties, and Molecular Function Information

Predrag Radivojac¹, Kang Peng¹, Wyatt T. Clark¹, Brandon J. Peters², Amrita Mohan¹, Sean M. Boyle¹ & Sean D. Mooney²

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1. School of Informatics, Indiana University
2. Center for Computational Biology and Bioinformatics, Department of Medical and Molecular Genetics, Indiana University School of Medicine

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Document Type:

Manuscript

Date:

Received 28 August 2008 00:24 UTC; Posted 29 August 2008

Subjects:

Bioinformatics

Tags:

• protein function prediction
• disease gene discovery
• protein interaction network
• disease ontology

Abstract:

One of the most important tasks of modern bioinformatics is the development of computational tools that can be used to understand and treat human disease. To date, a variety of methods have been explored and algorithms for predicting whether a protein is involved in disease are gaining in their utility. Here, we describe an algorithm for detecting protein-disease associations based on the human protein-protein interaction network, known gene-disease associations, protein sequence, and protein functional information at the molecular level. Our method, PhenoPred (www.phenopred.org), is supervised: first, we map each protein onto the spaces of disease and functional terms based on distance to all annotated proteins in the protein interaction network. We also encode sequence, function, physicochemical, and predicted structural properties, such as secondary structure and flexibility. We then train support vector machines to detect a protein’s disease function for a number of terms in Disease Ontology (DO). We provided evidence that, despite the noise/incompleteness of experimental data and unfinished ontology of diseases, identification of candidate genes and proteins can be successful even when a large number of candidate disease terms are predicted on simultaneously.

Collection:

AFP-Biosapiens 2008

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This document is licensed to the public under the Creative Commons Attribution 3.0 License

How to cite this document:

Radivojac, Predrag, Peng, Kang, Clark, Wyatt, Peters, Brandon, Mohan, Amrita, Boyle, Sean, and Mooney, Sean. Predicting Protein-Disease Relationships Using Sequence, Physicochemical Properties, and Molecular Function Information. Available from Nature Precedings <http://dx.doi.org/10.1038/npre.2008.2239.1> (2008)

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