Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction

Abstract

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.

arXiv commentTo appear in Findings of ACL 2021

References in corpus (7)

• BioBERT: a pre-trained biomedical language representation model for biomedical text mining
• Graph-Bert: Only Attention is Needed for Learning Graph Representations
• SciBERT: A Pretrained Language Model for Scientific Text
• GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation
• Relation extraction between the clinical entities based on the shortest dependency path based LSTM
• "When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware Attention Framework for Relationship Extraction
• Assigning function to protein-protein interactions: a weakly supervised BioBERT based approach using PubMed abstracts