ArticleDatabase : the journal of biological databases and curation2018
Improving the learning of chemical-protein interactions from literature using transfer learning and specialized word embeddings.
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 11 papers.
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Who cites it
11 citing papers in PubMed.
- Prompt Tuning in Biomedical Relation Extraction.Journal of healthcare informatics research · 2024Article
- Recent Advances in Electrochemical Biosensors: Applications, Challenges, and Future Scope.Biosensors · 2021Review
- Extraction of causal relations based on SBEL and BERT model.Database : the journal of biological databases and curation · 2021Article
- CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated multi-relational graph.BMC bioinformatics · 2020Article
- A Graph Convolutional Network-Based Method for Chemical-Protein Interaction Extraction: Algorithm Development.JMIR medical informatics · 2020Article
- Article
- Extraction of chemical-protein interactions from the literature using neural networks and narrow instance representation.Database : the journal of biological databases and curation · 2019Article
- Chemical-protein interaction extraction via contextualized word representations and multihead attention.Database : the journal of biological databases and curation · 2019Article
- Extracting chemical-protein interactions from literature using sentence structure analysis and feature engineering.Database : the journal of biological databases and curation · 2019Article
- Chemlistem: chemical named entity recognition using recurrent neural networks.Journal of cheminformatics · 2018Article
- Potent pairing: ensemble of long short-term memory networks and support vector machine for chemical-protein relation extraction.Database : the journal of biological databases and curation · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we explore the application of artificial neural network ('deep learning') methods to the problem of detecting chemical-protein interactions in PubMed abstracts. We present here a system using multiple Long Short Term Memory layers to analyse candidate interactions, to determine whether there is a relation and which type. A particular feature of our system is the use of unlabelled data, both to pre-train word embeddings and also pre-train LSTM layers in the neural network. On the BioCreative VI CHEMPROT test corpus, our system achieves an F score of 61.51% (56.10% precision, 67.84% recall).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.