ArticlePloS one2017
Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The influence of prompt engineering on large language models for protein-protein interaction identification in biomedical literature.Scientific reports · 2025Article
- Harnessing full-text publications for deep insights into C. elegans and Drosophila biomaps.BMC genomics · 2024Article
- Triage of documents containing protein interactions affected by mutations using an NLP based machine learning approach.BMC genomics · 2020Article
- The BioGRID interaction database: 2019 update.Nucleic acids research · 2019Article
- Extracting chemical-protein interactions from literature using sentence structure analysis and feature engineering.Database : the journal of biological databases and curation · 2019Article
- Automatic extraction of protein-protein interactions using grammatical relationship graph.BMC medical informatics and decision making · 2018Article
- Automatic extraction of gene-disease associations from literature using joint ensemble learning.PloS one · 2018Article
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Authors and funding
3 authors.
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Abstract
Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems.
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