Predicting receptor-ligand pairs through kernel learning

BMC Bioinformatics

Methodology Article

Bart De Moor¹  Ernesto Iacucci¹  Fabian Ojeda¹  Yves Moreau¹ 

[1] SCD-ESAT, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001, Leuven, Belgium;

关键词: Domain Content;    Multiple Data Source;    Phylogenetic Profile;    Kernel Learning;    Chemokine Family;   

DOI  :  10.1186/1471-2105-12-336

 received in 2011-02-21, accepted in 2011-08-11,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundRegulation of cellular events is, often, initiated via extracellular signaling. Extracellular signaling occurs when a circulating ligand interacts with one or more membrane-bound receptors. Identification of receptor-ligand pairs is thus an important and specific form of PPI prediction.ResultsGiven a set of disparate data sources (expression data, domain content, and phylogenetic profile) we seek to predict new receptor-ligand pairs. We create a combined kernel classifier and assess its performance with respect to the Database of Ligand-Receptor Partners (DLRP) 'golden standard' as well as the method proposed by Gertz et al. Among our findings, we discover that our predictions for the tgfβ family accurately reconstruct over 76% of the supported edges (0.76 recall and 0.67 precision) of the receptor-ligand bipartite graph defined by the DLRP "golden standard". In addition, for the tgfβ family, the combined kernel classifier is able to relatively improve upon the Gertz et al. work by a factor of approximately 1.5 when considering that our method has an F-measure of 0.71 while that of Gertz et al. has a value of 0.48.ConclusionsThe prediction of receptor-ligand pairings is a difficult and complex task. We have demonstrated that using kernel learning on multiple data sources provides a stronger alternative to the existing method in solving this task.

【 授权许可】

Unknown   
© Iacucci et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.