# Deep learning for extracting protein-protein interactions from biomedical literature

arXiv: https://arxiv.org/abs/1706.01556 · HTML: https://mlsanity.com/paper/1706.01556/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2017.

## Built on by (most cited first, 6 of 6)

- [Evaluation of GPT and BERT-based models on identifying protein-protein interactions in biomedical text](https://mlsanity.com/paper/2303.17728/) (2023)
- ["Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision](https://mlsanity.com/paper/2102.01222/) (2021)
- [Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge](https://mlsanity.com/paper/2307.08813/) (2023)
- [Extracting Protein-Protein Interactions (PPIs) from Biomedical Literature using Attention-based Relational Context Information](https://mlsanity.com/paper/2403.05602/) (2024)
- [Large-scale protein-protein post-translational modification extraction with distant supervision and confidence calibrated BioBERT](https://mlsanity.com/paper/2201.02229/) (2022)
- [Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction](https://mlsanity.com/paper/2107.00596/) (2021)

## Abstract (arXiv)

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 compares favorably to the state-of-the-art rich-feature and single-kernel based 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.