# Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction

arXiv: https://arxiv.org/abs/2107.00596 · HTML: https://mlsanity.com/paper/2107.00596/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Graph-Bert: Only Attention is Needed for Learning Graph Representations](https://mlsanity.com/paper/2001.05140/) (2020)
- [GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation](https://mlsanity.com/paper/1909.05729/) (2019)
- [Relation Extraction from Biomedical and Clinical Text: Unified Multitask Learning Framework](https://mlsanity.com/paper/2009.09509/) (2020)
- ["When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware Attention Framework for Relationship Extraction](https://mlsanity.com/paper/2009.10155/) (2020)
- [Deep learning for extracting protein-protein interactions from biomedical literature](https://mlsanity.com/paper/1706.01556/) (2017)
- [Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information](https://mlsanity.com/paper/1805.05593/) (2018)
- [Identifying Protein-Protein Interaction using Tree LSTM and Structured Attention](https://mlsanity.com/paper/1808.03227/) (2018)
- [Relation extraction between the clinical entities based on the shortest dependency path based LSTM](https://mlsanity.com/paper/1903.09941/) (2019)
- [Assigning function to protein-protein interactions: a weakly supervised BioBERT based approach using PubMed abstracts](https://mlsanity.com/paper/2008.08727/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Commonsense-Infused Language-Agnostic Learning Framework for Enhancing Prediction of Political Polarity in Multilingual News Headlines](https://mlsanity.com/paper/2212.00298/) (2022)

## Abstract (arXiv)

The recent advancement of pre-trained Transformer models has propelled the development of effective text mining models across various biomedical tasks. However, these models are primarily learned on the textual data and often lack the domain knowledge of the entities to capture the context beyond the sentence. In this study, we introduced a novel framework that enables the model to learn multi-omnics biological information about entities (proteins) with the help of additional multi-modal cues like molecular structure. Towards this, rather developing modality-specific architectures, we devise a generalized and optimized graph based multi-modal learning mechanism that utilizes the GraphBERT model to encode the textual and molecular structure information and exploit the underlying features of various modalities to enable end-to-end learning. We evaluated our proposed method on ProteinProtein Interaction task from the biomedical corpus, where our proposed generalized approach is observed to be benefited by the additional domain-specific modality.