Predicting protein relationships to human pathways through a relational learning approach based on simple sequence features
Editor: Ying Xu Authors: 
Beatriz García-Jiménez, Tirso Pons, Araceli Sanchis, Alfonso ValenciaAuthors Info & Claims
Pages 753 - 765
Abstract
Biological pathways are important elements of systems biology and in the past decade, an increasing number of pathway databases have been set up to document the growing understanding of complex cellular processes. Although more genome-sequence data are becoming available, a large fraction of it remains functionally uncharacterized. Thus, it is important to be able to predict the mapping of poorly annotated proteins to original pathway models. Results: We have developed a Relational Learning-based Extension (RLE) system to investigate pathway membership through a function prediction approach that mainly relies on combinations of simple properties attributed to each protein. RLE searches for proteins with molecular similarities to specific pathway components. Using RLE, we associated 383 uncharacterized proteins to 28 pre-defined human Reactome pathways, demonstrating relative confidence after proper evaluation. Indeed, in specific cases manual inspection of the database annotations and the related literature supported the proposed classifications. Examples of possible additional components of the Electron transport system, Telomere maintenance and Integrin cell surface interactions pathways are discussed in detail. Availability: All the human predicted proteins in the 2009 and 2012 releases 30 and 40 of Reactome are available at http://rle.bioinfo.cnio.es.
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- Predicting protein relationships to human pathways through a relational learning approach based on simple sequence features
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Published: 01 July 2014
Accepted: 03 April 2014
Revised: 02 April 2014
Received: 21 December 2012
Published in TCBB Volume 11, Issue 4
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