ArticleDatabase : the journal of biological databases and curation2016
PIPE: a protein-protein interaction passage extraction module for BioCreative challenge.
Article in Database : the journal of biological databases and curation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- The influence of prompt engineering on large language models for protein-protein interaction identification in biomedical literature.Scientific reports · 2025Article
- Large language model based framework for automated extraction of genetic interactions from unstructured data.PloS one · 2024Article
- A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles.Database : the journal of biological databases and curation · 2022Article
- Triage of documents containing protein interactions affected by mutations using an NLP based machine learning approach.BMC genomics · 2020Article
- HMNPPID-human malignant neoplasm protein-protein interaction database.Human genomics · 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
- LPTK: a linguistic pattern-aware dependency tree kernel approach for the BioCreative VI CHEMPROT task.Database : the journal of biological databases and curation · 2018Article
- Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature.PloS one · 2017Article
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Authors and funding
5 authors.
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Abstract
Identifying the interactions between proteins mentioned in biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this article, we propose PIPE, an interaction pattern generation module used in the Collaborative Biocurator Assistant Task at BioCreative V (http://www.biocreative.org/) to capture frequent protein-protein interaction (PPI) patterns within text. We also present an interaction pattern tree (IPT) kernel method that integrates the PPI patterns with convolution tree kernel (CTK) to extract PPIs. Methods were evaluated on LLL, IEPA, HPRD50, AIMed and BioInfer corpora using cross-validation, cross-learning and cross-corpus evaluation. Empirical evaluations demonstrate that our method is effective and outperforms several well-known PPI extraction methods. DATABASE URL.
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