Deep learning for extracting protein-protein interactions from biomedical literature

By Zhiyong Lu. 2017

109 citations recorded by the source corpus.

State-of-the-art methods for protein-protein interaction (PPI) extraction are primarily feature-based or kernel-based by leveraging lexical and syntactic information. But how to incorporate such knowledge in the recent deep learning methods remains an open question. In this paper, we propose a multichannel dependency-based convolutional neural network model (McDepCNN). It applies one channel to the embedding vector of each word in the sentence, and another channel to the embedding vector of the head of the corresponding word. Therefore, the model can use richer information obtained from different channels. Experiments on two public benchmarking datasets, AIMed and BioInfer, demonstrate that McDepCNN provides up to 6% F1-score improvement over rich feature-based methods and single-kernel methods. In addition, McDepCNN achieves 24.4% relative improvement in F1-score over the state-of-the-art methods on cross-corpus evaluation and 12% improvement in F1-score over kernel-based methods on “difficult” instances. These results suggest that McDepCNN generalizes more easily over different corpora, and is capable of capturing long distance features in the sentences.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval (2018, methodology): “While automated information extraction has been applied to simple relationships — such as Drug-Drug (Asada et al., 2017) or Protein-Protein (Peng and Lu, 2017); (Peng et al., 2015); (Li et al., 2017) interaction — with adequate precision and recall, clinically actionable biomarkers need to satisfy r”
AI in Health: State of the Art, Challenges, and Future Directions (2019, methodology): “Peng and Lu [104] developed a multichannel dependency-based CNN approach for extracting protein-protein interactions from biomedical literature searches and achieved a 24.”