ArticleDatabase : the journal of biological databases and curation2018
LPTK: a linguistic pattern-aware dependency tree kernel approach for the BioCreative VI CHEMPROT task.
Article in Database : the journal of biological databases and curation, 2018. 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, 23 citations in OpenAlex.
- Prompt Tuning in Biomedical Relation Extraction.Journal of healthcare informatics research · 2024Article
- Recent advances in biomedical literature mining.Briefings in bioinformatics · 2021Review
- A Graph Convolutional Network-Based Method for Chemical-Protein Interaction Extraction: Algorithm Development.JMIR medical informatics · 2020Article
- Article
- Constructing knowledge graphs and their biomedical applications.Computational and structural biotechnology journal · 2020Review
- Chemical-protein interaction extraction via contextualized word representations and multihead attention.Database : the journal of biological databases and curation · 2019Article
- Extraction of chemical-protein interactions from the literature using neural networks and narrow instance representation.Database : the journal of biological databases and curation · 2019Article
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3 authors at 3 institutions in 1 country.
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Abstract
Identifying the interactions between chemical compounds and genes from biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this paper, we describe Linguistic Pattern-Aware Dependency Tree Kernel, a linguistic interaction pattern learning method developed for CHEMPROT task-BioCreative VI, to capture chemical-protein interaction (CPI) patterns within biomedical literatures. We also introduce a framework to integrate these linguistic patterns with smooth partial tree kernel to extract the CPIs. This new method of feature representation models aspects of linguistic probability in geometric representation, which not only optimizes the sufficiency of feature dimension for classification, but also defines features as interpretable contexts rather than long vectors of numbers. In order to test the robustness and efficiency of our system in identifying different kinds of biological interactions, we evaluated our framework on three separate data sets, i.e. CHEMPROT corpus, Chemical-Disease Relation corpus and Protein-Protein Interaction corpus. Corresponding experiment results demonstrate that our method is effective and outperforms several compared systems for each data set.
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