Walk-weighted subsequence kernels for protein-protein interaction extraction

BMC Bioinformatics

Research Article

Juntae Yoon¹  Seog Park²  Seonho Kim²  Jihoon Yang² 

[1] Daumsoft Inc, Se-Ah Venture Tower, Seoul, Korea;Department of Computer Science, Sogang University, Seoul, Korea;

关键词: Short Path;    Relation Extraction;    Syntactic Information;    Syntactic Relation;    String Kernel;   

DOI  :  10.1186/1471-2105-11-107

 received in 2009-04-22, accepted in 2010-02-25,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundThe construction of interaction networks between proteins is central to understanding the underlying biological processes. However, since many useful relations are excluded in databases and remain hidden in raw text, a study on automatic interaction extraction from text is important in bioinformatics field.ResultsHere, we suggest two kinds of kernel methods for genic interaction extraction, considering the structural aspects of sentences. First, we improve our prior dependency kernel by modifying the kernel function so that it can involve various substructures in terms of (1) e-walks, (2) partial match, (3) non-contiguous paths, and (4) different significance of substructures. Second, we propose the walk-weighted subsequence kernel to parameterize non-contiguous syntactic structures as well as semantic roles and lexical features, which makes learning structural aspects from a small amount of training data effective. Furthermore, we distinguish the significances of parameters such as syntactic locality, semantic roles, and lexical features by varying their weights.ConclusionsWe addressed the genic interaction problem with various dependency kernels and suggested various structural kernel scenarios based on the directed shortest dependency path connecting two entities. Consequently, we obtained promising results over genic interaction data sets with the walk-weighted subsequence kernel. The results are compared using automatically parsed third party protein-protein interaction (PPI) data as well as perfectly syntactic labeled PPI data.

【 授权许可】

CC BY   
© Kim et al; licensee BioMed Central Ltd. 2010