論文

査読有り 筆頭著者
2022年6月24日

SPARSE: a sparse hypergraph neural network for learning multiple types of latent combinations to accurately predict drug–drug interactions

Bioinformatics
  • Duc Anh Nguyen
  • ,
  • Canh Hao Nguyen
  • ,
  • Peter Petschner
  • ,
  • Hiroshi Mamitsuka

38
Supplement_1
開始ページ
i333
終了ページ
i341
記述言語
掲載種別
研究論文(学術雑誌)
DOI
10.1093/bioinformatics/btac250
出版者・発行元
Oxford University Press (OUP)

Abstract

Motivation

Predicting side effects of drug–drug interactions (DDIs) is an important task in pharmacology. The state-of-the-art methods for DDI prediction use hypergraph neural networks to learn latent representations of drugs and side effects to express high-order relationships among two interacting drugs and a side effect. The idea of these methods is that each side effect is caused by a unique combination of latent features of the corresponding interacting drugs. However, in reality, a side effect might have multiple, different mechanisms that cannot be represented by a single combination of latent features of drugs. Moreover, DDI data are sparse, suggesting that using a sparsity regularization would help to learn better latent representations to improve prediction performances.

Results

We propose SPARSE, which encodes the DDI hypergraph and drug features to latent spaces to learn multiple types of combinations of latent features of drugs and side effects, controlling the model sparsity by a sparse prior. Our extensive experiments using both synthetic and three real-world DDI datasets showed the clear predictive performance advantage of SPARSE over cutting-edge competing methods. Also, latent feature analysis over unknown top predictions by SPARSE demonstrated the interpretability advantage contributed by the model sparsity.

Availability and implementation

Code and data can be accessed at https://github.com/anhnda/SPARSE.

Supplementary information

Supplementary data are available at Bioinformatics online.

リンク情報
DOI
https://doi.org/10.1093/bioinformatics/btac250
URL
https://academic.oup.com/bioinformatics/article-pdf/38/Supplement_1/i333/49887274/btac250.pdf
ID情報
  • DOI : 10.1093/bioinformatics/btac250
  • ISSN : 1367-4803
  • eISSN : 1367-4811

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