3 research outputs found
BERTChem-DDI : Improved Drug-Drug Interaction Prediction from text using Chemical Structure Information
Traditional biomedical version of embeddings obtained from pre-trained
language models have recently shown state-of-the-art results for relation
extraction (RE) tasks in the medical domain. In this paper, we explore how to
incorporate domain knowledge, available in the form of molecular structure of
drugs, for predicting Drug-Drug Interaction from textual corpus. We propose a
method, BERTChem-DDI, to efficiently combine drug embeddings obtained from the
rich chemical structure of drugs along with off-the-shelf domain-specific
BioBERT embedding-based RE architecture. Experiments conducted on the
DDIExtraction 2013 corpus clearly indicate that this strategy improves other
strong baselines architectures by 3.4\% macro F1-score.Comment: arXiv admin note: substantial text overlap with arXiv:2012.1114
Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach
Predicting and discovering drug-drug interactions (DDIs) using machine
learning has been studied extensively. However, most of the approaches have
focused on text data or textual representation of the drug structures. We
present the first work that uses multiple data sources such as drug structure
images, drug structure string representation and relational representation of
drug relationships as the input. To this effect, we exploit the recent advances
in deep networks to integrate these varied sources of inputs in predicting
DDIs. Our empirical evaluation against several state-of-the-art methods using
standalone different data types for drugs clearly demonstrate the efficacy of
combining heterogeneous data in predicting DDIs.Comment: 10 pages, 6 figures, Accepted as a short paper to 'Artificial
Intelligence in Medicine 2021
Towards Incorporating Entity-specific Knowledge Graph Information in Predicting Drug-Drug Interactions
Off-the-shelf biomedical embeddings obtained from the recently released
various pre-trained language models (such as BERT, XLNET) have demonstrated
state-of-the-art results (in terms of accuracy) for the various natural
language understanding tasks (NLU) in the biomedical domain. Relation
Classification (RC) falls into one of the most critical tasks. In this paper,
we explore how to incorporate domain knowledge of the biomedical entities (such
as drug, disease, genes), obtained from Knowledge Graph (KG) Embeddings, for
predicting Drug-Drug Interaction from textual corpus. We propose a new method,
BERTKG-DDI, to combine drug embeddings obtained from its interaction with other
biomedical entities along with domain-specific BioBERT embedding-based RC
architecture. Experiments conducted on the DDIExtraction 2013 corpus clearly
indicate that this strategy improves other baselines architectures by 4.1%
macro F1-score