ArticleBioMed research international2016
A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction.
Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- The influence of prompt engineering on large language models for protein-protein interaction identification in biomedical literature.Scientific reports · 2025Article
- GNNMF: a multi-view graph neural network for ATAC-seq motif finding.BMC genomics · 2024Article
- Protein-Protein Interaction Network Extraction Using Text Mining Methods Adds Insight into Autism Spectrum Disorder.Biology · 2023Article
- Relation Extraction in Biomedical Texts Based on Multi-Head Attention Model With Syntactic Dependency Feature: Modeling Study.JMIR medical informatics · 2022Article
- MarkerGenie: an NLP-enabled text-mining system for biomedical entity relation extraction.Bioinformatics advances · 2022Article
- Extraction of causal relations based on SBEL and BERT model.Database : the journal of biological databases and curation · 2021Article
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- Bio-semantic relation extraction with attention-based external knowledge reinforcement.BMC bioinformatics · 2020Article
- Named Entity Recognition and Relation Detection for Biomedical Information Extraction.Frontiers in cell and developmental biology · 2020Review
- Incorporating Deep Learning With Word Embedding to Identify Plant Ubiquitylation Sites.Frontiers in cell and developmental biology · 2020Article
- Using distant supervision to augment manually annotated data for relation extraction.PloS one · 2019Article
- The extraction of complex relationships and their conversion to biological expression language (BEL) overview of the BioCreative VI (2017) BEL track.Database : the journal of biological databases and curation · 2019Article
- Opportunities and obstacles for deep learning in biology and medicine.Journal of the Royal Society, Interface · 2018Review
- Extracting chemical-protein relations with ensembles of SVM and deep learning models.Database : the journal of biological databases and curation · 2018Article
- Evaluating Casama: Contextualized semantic maps for summarization of lung cancer studies.Computers in biology and medicine · 2018Article
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2 authors.
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Abstract
The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method (1) only takes the sdp and word embedding as input and (2) could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task.
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