# BactInt: A domain driven transfer learning approach and a corpus for extracting inter-bacterial interactions from biomedical text

arXiv: https://arxiv.org/abs/2305.07468 · HTML: https://mlsanity.com/paper/2305.07468/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 55 references that are in the map.
- First posted on arXiv 27 Apr 2023.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining](https://mlsanity.com/paper/2210.10341/) (2022)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)

## Abstract (arXiv)

The community of different types of microbes present in a biological niche plays a very important role in functioning of the system. The crosstalk or interactions among the different microbes contributes to the building blocks of such microbial community structures. Evidence reported in biomedical text serves as a reliable source for predicting such interactions. However, going through the vast and ever-increasing volume of biomedical literature is an intimidating and time consuming process. This necessitates development of automated methods capable of accurately extracting bacterial relations reported in biomedical literature. In this paper, we introduce a method for automated extraction of microbial interactions (specifically between bacteria) from biomedical literature along with ways of using transfer learning to improve its accuracy. We also describe a pipeline using which relations among specific bacteria groups can be mined. Additionally, we introduce the first publicly available dataset which can be used to develop bacterial interaction extraction methods.