Abstract
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.
Highlights
The construction of interaction networks between proteins is central to understanding the underlying biological processes
The results revealed that the walk kernel, one of the feature-based kernels, showed a very competitive performance on Learning Language in Logic (LLL) data [15], with an F-score of 77.5
We used the LLL 05 shared task data for individual evaluation of the kernels that we proposed in this work
Summary
The construction of interaction networks between proteins is central to understanding the underlying biological processes. 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. Introduction In recent years, biomedical research has been accelerated by technological advances in biomedical science and a surge of genomic data from the Human Genome Project and a huge amount of new information has been coming from the related research. Since a majority of databases still rely on human curators, substantial manual efforts are required to cope with enormous collections on biomedical research and publications. The development of high-quality information extraction tools that allow scientists and curators to quickly access new discoveries is an important issue in bioinformatics. Biomedical information extraction usually involves the recognition of biomedical entities and pre-defined types of facts, such as relations/interactions between the entities, through the analysis of raw textual data.
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