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Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature
Gurusamy Murugesan1, Sabenabanu Abdulkadhar1, Jeyakumar Natarajan1
1Data Mining and Text Mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, Tamilnadu, India.
Plos One
|November 4, 2017
Summary
This study introduces a novel Distributed Smoothed Tree kernel (DSTK) for extracting protein-protein interactions (PPIs) from text. DSTK improves accuracy by combining syntactic and semantic information, outperforming existing methods.
Area of Science:
- Computational Biology
- Bioinformatics
- Natural Language Processing
Background:
- Automatic extraction of protein-protein interactions (PPIs) is crucial for understanding biological pathways.
- Existing kernel-based methods often struggle to capture semantic relationships between interacting proteins.
Purpose of the Study:
- To develop a novel tree kernel, the Distributed Smoothed Tree kernel (DSTK), for enhanced PPI extraction.
- To integrate syntactic and semantic information for improved accuracy in identifying PPIs.
Main Methods:
- The proposed DSTK method utilizes distributed trees with syntactic information and distributional semantic vectors.
- An ensemble support vector machine (SVM) model was created by combining feature-based kernels with DSTK.
- The system was evaluated on five benchmark corpora: AIMed, BioInfer, HPRD50, IEPA, and LLL.
Main Results:
- The DSTK-based system achieved superior performance across all five corpora.
- Experimental results demonstrated a significant improvement in f-score compared to state-of-the-art systems.
- The combination of syntactic and semantic information proved effective for PPI extraction.
Conclusions:
- The Distributed Smoothed Tree kernel (DSTK) offers a robust approach for protein-protein interaction extraction.
- Integrating semantic vectors with syntactic structures enhances the accuracy of biological information extraction.
- This method advances the field of automated biomedical literature analysis for PPI identification.
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