  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.
