CentSmoothie: Central-Smoothing Hypergraph Neural Networks for Predicting Drug-Drug Interactions

12/15/2021
by   Duc Anh Nguyen, et al.
0

Predicting drug-drug interactions (DDI) is the problem of predicting side effects (unwanted outcomes) of a pair of drugs using drug information and known side effects of many pairs. This problem can be formulated as predicting labels (i.e. side effects) for each pair of nodes in a DDI graph, of which nodes are drugs and edges are interacting drugs with known labels. State-of-the-art methods for this problem are graph neural networks (GNNs), which leverage neighborhood information in the graph to learn node representations. For DDI, however, there are many labels with complicated relationships due to the nature of side effects. Usual GNNs often fix labels as one-hot vectors that do not reflect label relationships and potentially do not obtain the highest performance in the difficult cases of infrequent labels. In this paper, we formulate DDI as a hypergraph where each hyperedge is a triple: two nodes for drugs and one node for a label. We then present CentSmoothie, a hypergraph neural network that learns representations of nodes and labels altogether with a novel central-smoothing formulation. We empirically demonstrate the performance advantages of CentSmoothie in simulations as well as real datasets.

READ FULL TEXT
research
06/25/2022

HyGNN: Drug-Drug Interaction Prediction via Hypergraph Neural Network

Drug-Drug Interactions (DDIs) may hamper the functionalities of drugs, a...
research
08/30/2022

Graph Distance Neural Networks for Predicting Multiple Drug Interactions

Since multidrug combination is widely applied, the accurate prediction o...
research
04/01/2020

Drug-disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data

Adverse Drug Reaction (ADR) is a significant public health concern world...
research
02/17/2023

Modeling Polypharmacy and Predicting Drug-Drug Interactions using Deep Generative Models on Multimodal Graphs

Latent representations of drugs and their targets produced by contempora...
research
08/05/2022

NRBdMF: A recommendation algorithm for predicting drug effects considering directionality

Predicting the novel effects of drugs based on information about approve...
research
12/01/2021

Structure-Aware Label Smoothing for Graph Neural Networks

Representing a label distribution as a one-hot vector is a common practi...
research
06/05/2023

Classification of Edge-dependent Labels of Nodes in Hypergraphs

A hypergraph is a data structure composed of nodes and hyperedges, where...

Please sign up or login with your details

Forgot password? Click here to reset