# Identifying Protein-Protein Interaction using Tree LSTM and Structured Attention

arXiv: https://arxiv.org/abs/1808.03227 · HTML: https://mlsanity.com/paper/1808.03227/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2018.

## Built on by (most cited first, 5 of 5)

- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [Evaluation of GPT and BERT-based models on identifying protein-protein interactions in biomedical text](https://mlsanity.com/paper/2303.17728/) (2023)
- [Domain adaptation in small-scale and heterogeneous biological datasets](https://mlsanity.com/paper/2405.19221/) (2024)
- [Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction](https://mlsanity.com/paper/2107.00596/) (2021)
- [Context-Aware Scientific Knowledge Extraction on Linked Open Data using Large Language Models](https://mlsanity.com/paper/2506.17580/) (2025)

## Abstract (arXiv)

Identifying interactions between proteins is important to understand underlying biological processes. Extracting a protein-protein interaction (PPI) from the raw text is often very difficult. Previous supervised learning methods have used handcrafted features on human-annotated data sets. In this paper, we propose a novel tree recurrent neural network with structured attention architecture for doing PPI. Our architecture achieves state of the art results (precision, recall, and F1-score) on the AIMed and BioInfer benchmark data sets. Moreover, our models achieve a significant improvement over previous best models without any explicit feature extraction. Our experimental results show that traditional recurrent networks have inferior performance compared to tree recurrent networks for the supervised PPI problem.