Predicting Protein-Protein Interactions by Association Mining

Identifying protein-protein interactions is a key problem in molecular biology. Currently, interactions cannot be reliably predicted on a proteome-wide scale but direct and indirect evidence for interactions is increasingly available from high-throughput interaction detection methods, gene expressio...

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Bibliographic Details
Published inInformation systems frontiers Vol. 8; no. 1; pp. 37 - 47
Main Authors Kotlyar, Max, Jurisca, Igor
Format Journal Article
LanguageEnglish
Published New York Springer Nature B.V 01.02.2006
Subjects
Algorithms
Datasets
Gene expression
Information systems
Information technology
Maple syrup
Molecular biology
Predictions
Proteins
Signal transduction
Statistical methods
Studies
Variables
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Summary:Identifying protein-protein interactions is a key problem in molecular biology. Currently, interactions cannot be reliably predicted on a proteome-wide scale but direct and indirect evidence for interactions is increasingly available from high-throughput interaction detection methods, gene expression microarrays, and protein annotation projects. In this paper we propose an association mining approach to integrating these diverse types of evidence. We apply this approach to a number of datasets consisting of interacting and non-interacting protein pairs annotated with different types of evidence. We identify patterns that distinguish interacting and non-interacting protein pairs, and use these patterns to assign a confidence level to proposed interactions. [PUBLICATION ABSTRACT]
Bibliography:ObjectType-Article-2
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ISSN:1387-3326
1572-9419
DOI:10.1007/s10796-005-6102-8