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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
DDI Prediction via Heterogeneous Graph Attention Networks
Polypharmacy, defined as the use of multiple drugs together, is a standard treatment method, especially for severe and chronic diseases. However, using multiple drugs together may cause interactions between drugs. Drug-d…
DecoderGraph AttentionPredictionPredicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI
Drug-drug interactions (DDIs) are a major concern in clinical practice, as they can lead to reduced therapeutic efficacy or severe adverse effects. Traditional computational approaches often struggle to capture the compl…
Graph Representation LearningGraph Neural NetworkDrug-target interaction prediction by integrating heterogeneous information with mutual attention network
Identification of drug-target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target pro…
Drug DiscoveryGraph AttentionPredictionHeter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning
Drug repositioning offers an effective solution to drug discovery, saving both time and resources by finding new indications for existing drugs. Typically, a drug takes effect via its protein targets in the cell. As a re…
Data IntegrationDrug DiscoveryHierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug Interactions
Most existing methods for predicting drug-drug interactions (DDI) predominantly concentrate on capturing the explicit relationships among drugs, overlooking the valuable implicit correlations present between drug pairs (…
Graph Representation LearningRepresentation Learning