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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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>Code and data can be accessed at https:\/\/github.com\/anhnda\/SPARSE.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac250","type":"journal-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T11:10:15Z","timestamp":1649934615000},"page":"i333-i341","source":"Crossref","is-referenced-by-count":16,"title":["SPARSE: a sparse hypergraph neural network for learning multiple types of latent combinations to accurately predict drug\u2013drug interactions"],"prefix":"10.1093","volume":"38","author":[{"given":"Duc Anh","family":"Nguyen","sequence":"first","affiliation":[{"name":"Bioinformatics Center, Institute for Chemical Research, Kyoto University , Uji, Japan"}]},{"given":"Canh Hao","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Bioinformatics Center, Institute for Chemical Research, Kyoto University , Uji, Japan"}]},{"given":"Peter","family":"Petschner","sequence":"additional","affiliation":[{"name":"Bioinformatics Center, Institute for Chemical Research, Kyoto University , Uji, Japan"},{"name":"Department of Pharmacodynamics, Semmelweis University , Budapest, Hungary"}]},{"given":"Hiroshi","family":"Mamitsuka","sequence":"additional","affiliation":[{"name":"Bioinformatics Center, Institute for Chemical Research, Kyoto University , Uji, Japan"},{"name":"Department of Computer Science, Aalto University , Espoo, Finland"}]}],"member":"286","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"key":"2023041407570982400_","first-page":"2239","article-title":"A tensor approach to learning mixed membership community models","author":"Anandkumar","year":"2014"},{"key":"2023041407570982400_","doi-asserted-by":"crossref","first-page":"2457","DOI":"10.1007\/s00204-018-2244-6","article-title":"Opioid analgesic drugs and serotonin toxicity (syndrome): mechanisms, animal models, and links to clinical effects","volume":"92","author":"Baldo","year":"2018","journal-title":"Arch. 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