NDLI: Predicting protein relationships to human pathways through a relational learning approach based on simple sequence features

Content Provider	ACM Digital Library
Author	Valencia, Alfonso Sanchis, Araceli Pons, Tirso García-jiménez, Beatriz
Copyright Year	2014
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.
Starting Page	753
Ending Page	765
Page Count	13
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2014.2318730
Volume Number	11
Issue Number	4
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-07-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Function prediction Human reactome pathways Knowledge relational representation Machine learning Pathway relationship prediction Sequence-based prediction
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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