Title
Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach
Abstract
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.
Year
DOI
Venue
2021
10.1007/978-3-030-77211-6_28
AIME
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Devendra Singh Dhami104.06
Siwen Yan200.34
Gautam Kunapuli300.68
David Page453361.12
Sriraam Natarajan5573.99