{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:40:27Z","timestamp":1773157227373,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T00:00:00Z","timestamp":1716163200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T00:00:00Z","timestamp":1716163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272138"],"award-info":[{"award-number":["62272138"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276059"],"award-info":[{"award-number":["62276059"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017366","name":"Key Research and Development Program of Heilongjiang","doi-asserted-by":"publisher","award":["2022ZX01A29"],"award-info":[{"award-number":["2022ZX01A29"]}],"id":[{"id":"10.13039\/100017366","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","award":["YQ2023F001"],"award-info":[{"award-number":["YQ2023F001"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>The identification of drug side effects plays a critical role in drug repositioning and drug screening. While clinical experiments yield accurate and reliable information about drug-related side effects, they are costly and time-consuming. Computational models have emerged as a promising alternative to predict the frequency of drug-side effects. However, earlier research has primarily centered on extracting and utilizing representations of drugs, like molecular structure or interaction graphs, often neglecting the inherent biomedical semantics of drugs and side effects.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>To address the previously mentioned issue, we introduce a hybrid multi-modal fusion framework (HMMF) for predicting drug side effect frequencies. Considering the wealth of biological and chemical semantic information related to drugs and side effects, incorporating multi-modal information offers additional, complementary semantics. HMMF utilizes various encoders to understand molecular structures, biomedical textual representations, and attribute similarities of both drugs and side effects. It then models drug-side effect interactions using both coarse and fine-grained fusion strategies, effectively integrating these multi-modal features.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>HMMF exhibits the ability to successfully detect previously unrecognized potential side effects, demonstrating superior performance over existing state-of-the-art methods across various evaluation metrics, including root mean squared error and area under receiver operating characteristic curve, and shows remarkable performance in cold-start scenarios.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-024-05806-6","type":"journal-article","created":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T10:01:58Z","timestamp":1716199318000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["HMMF: a hybrid multi-modal fusion framework for predicting drug side effect frequencies"],"prefix":"10.1186","volume":"25","author":[{"given":"Wuyong","family":"Liu","sequence":"first","affiliation":[]},{"given":"Jingyu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Guanyu","family":"Qiao","sequence":"additional","affiliation":[]},{"given":"Jilong","family":"Bian","sequence":"additional","affiliation":[]},{"given":"Benzhi","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,20]]},"reference":[{"issue":"9237","key":"5806_CR1","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1016\/S0140-6736(00)02799-9","volume":"356","author":"IR Edwards","year":"2000","unstructured":"Edwards IR, Aronson JK. Adverse drug reactions: definitions, diagnosis, and management. Lancet. 2000;356(9237):1255\u20139.","journal-title":"Lancet"},{"issue":"21","key":"5806_CR2","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1016\/S1359-6446(05)03632-9","volume":"10","author":"S Whitebread","year":"2005","unstructured":"Whitebread S, Hamon J, Bojanic D, Urban L. Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development. Drug Discovery Today. 2005;10(21):1421\u201333.","journal-title":"Drug Discovery Today"},{"key":"5806_CR3","doi-asserted-by":"crossref","unstructured":"Yao W, Zhao W, Jiang X, Shen X, He T. MPGNN-DSA: a meta-path-based graph neural network for drug-side effect association prediction. In: 2022 IEEE international conference on bioinformatics and biomedicine (BIBM), 2022; pp. 627\u2013632. IEEE","DOI":"10.1109\/BIBM55620.2022.9995486"},{"issue":"1","key":"5806_CR4","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1038\/s41540-022-00221-0","volume":"8","author":"P Paci","year":"2022","unstructured":"Paci P, Fiscon G, Conte F, Wang R-S, Handy DE, Farina L, Loscalzo J. Comprehensive network medicine-based drug repositioning via integration of therapeutic efficacy and side effects. npj Syst Biol Appl. 2022;8(1):12.","journal-title":"npj Syst Biol Appl"},{"issue":"2","key":"5806_CR5","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1109\/TCBB.2018.2850884","volume":"17","author":"H Jiang","year":"2018","unstructured":"Jiang H, Qiu Y, Hou W, Cheng X, Yim MY, Ching W-K. Drug side-effect profiles prediction: from empirical to structural risk minimization. IEEE\/ACM Trans Comput Biol Bioinf. 2018;17(2):402\u201310.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"5806_CR6","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J. node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 2016; pp. 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"issue":"6","key":"5806_CR7","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1093\/bib\/bbac458","volume":"23","author":"Y Qian","year":"2022","unstructured":"Qian Y, Ding Y, Zou Q, Guo F. Identification of drug-side effect association via restricted Boltzmann machines with penalized term. Brief Bioinform. 2022;23(6):458.","journal-title":"Brief Bioinform"},{"issue":"1","key":"5806_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-12-169","volume":"12","author":"E Pauwels","year":"2011","unstructured":"Pauwels E, Stoven V, Yamanishi Y. Predicting drug side-effect profiles: a chemical fragment-based approach. BMC Bioinform. 2011;12(1):1\u201313.","journal-title":"BMC Bioinform"},{"key":"5806_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2022.104131","volume":"132","author":"X Liang","year":"2022","unstructured":"Liang X, Li J, Fu Y, Qu L, Tan Y, Zhang P. A novel machine learning model based on sparse structure learning with adaptive graph regularization for predicting drug side effects. J Biomed Inform. 2022;132: 104131.","journal-title":"J Biomed Inform"},{"key":"5806_CR10","doi-asserted-by":"crossref","unstructured":"Jahid MJ, Ruan J. An ensemble approach for drug side effect prediction. In: 2013 IEEE international conference on bioinformatics and biomedicine, 2013; pp. 440\u2013445. IEEE","DOI":"10.1109\/BIBM.2013.6732532"},{"issue":"13","key":"5806_CR11","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1093\/bioinformatics\/btt234","volume":"29","author":"Y Wang","year":"2013","unstructured":"Wang Y, Zeng J. Predicting drug-target interactions using restricted Boltzmann machines. Bioinformatics. 2013;29(13):126\u201334.","journal-title":"Bioinformatics"},{"key":"5806_CR12","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.neucom.2018.01.085","volume":"287","author":"W Zhang","year":"2018","unstructured":"Zhang W, Liu X, Chen Y, Wu W, Wang W, Li X. Feature-derived graph regularized matrix factorization for predicting drug side effects. Neurocomputing. 2018;287:154\u201362.","journal-title":"Neurocomputing"},{"issue":"1","key":"5806_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-18305-y","volume":"11","author":"D Galeano","year":"2020","unstructured":"Galeano D, Li S, Gerstein M, Paccanaro A. Predicting the frequencies of drug side effects. Nat Commun. 2020;11(1):1\u201314.","journal-title":"Nat Commun"},{"issue":"1","key":"5806_CR14","first-page":"47","volume":"21","author":"X Chen","year":"2020","unstructured":"Chen X, Guan N-N, Sun Y-Z, Li J-Q, Qu J. Microrna-small molecule association identification: from experimental results to computational models. Brief Bioinform. 2020;21(1):47\u201361.","journal-title":"Brief Bioinform"},{"issue":"3","key":"5806_CR15","doi-asserted-by":"publisher","first-page":"061","DOI":"10.1093\/bib\/bbaa061","volume":"22","author":"C-C Wang","year":"2021","unstructured":"Wang C-C, Zhao Y, Chen X. Drug-pathway association prediction: from experimental results to computational models. Brief Bioinform. 2021;22(3):061.","journal-title":"Brief Bioinform"},{"issue":"21","key":"5806_CR16","first-page":"1","volume":"19","author":"S Dey","year":"2018","unstructured":"Dey S, Luo H, Fokoue A, Hu J, Zhang P. Predicting adverse drug reactions through interpretable deep learning framework. BMC Bioinform. 2018;19(21):1\u201313.","journal-title":"BMC Bioinform"},{"issue":"20","key":"5806_CR17","doi-asserted-by":"publisher","first-page":"3668","DOI":"10.3390\/molecules24203668","volume":"24","author":"B Hu","year":"2019","unstructured":"Hu B, Wang H, Yu Z. Drug side-effect prediction via random walk on the signed heterogeneous drug network. Molecules. 2019;24(20):3668.","journal-title":"Molecules"},{"issue":"3","key":"5806_CR18","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1093\/bib\/bbac126","volume":"23","author":"P Xuan","year":"2022","unstructured":"Xuan P, Wang M, Liu Y, Wang D, Zhang T, Nakaguchi T. Integrating specific and common topologies of heterogeneous graphs and pairwise attributes for drug-related side effect prediction. Brief Bioinform. 2022;23(3):126.","journal-title":"Brief Bioinform"},{"issue":"2","key":"5806_CR19","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1093\/bib\/bbab586","volume":"23","author":"X Xu","year":"2022","unstructured":"Xu X, Yue L, Li B, Liu Y, Wang Y, Zhang W, Wang L. DSGAT: predicting frequencies of drug side effects by graph attention networks. Brief Bioinform. 2022;23(2):586.","journal-title":"Brief Bioinform"},{"issue":"9","key":"5806_CR20","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1093\/bioinformatics\/btad532","volume":"39","author":"L Wang","year":"2023","unstructured":"Wang L, Sun C, Xu X, Li J, Zhang W. A neighborhood-regularization method leveraging multiview data for predicting the frequency of drug-side effects. Bioinformatics. 2023;39(9):532.","journal-title":"Bioinformatics"},{"issue":"6","key":"5806_CR21","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1093\/bib\/bbab239","volume":"22","author":"H Zhao","year":"2021","unstructured":"Zhao H, Zheng K, Li Y, Wang J. A novel graph attention model for predicting frequencies of drug-side effects from multi-view data. Brief Bioinform. 2021;22(6):239.","journal-title":"Brief Bioinform"},{"issue":"1","key":"5806_CR22","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1093\/bib\/bbab449","volume":"23","author":"H Zhao","year":"2022","unstructured":"Zhao H, Wang S, Zheng K, Zhao Q, Zhu F, Wang J. A similarity-based deep learning approach for determining the frequencies of drug side effects. Brief Bioinform. 2022;23(1):449.","journal-title":"Brief Bioinform"},{"issue":"1","key":"5806_CR23","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1093\/bib\/bbad445","volume":"25","author":"Y Zhao","year":"2024","unstructured":"Zhao Y, Yin J, Zhang L, Zhang Y, Chen X. Drug-drug interaction prediction: databases, web servers and computational models. Brief Bioinform. 2024;25(1):445.","journal-title":"Brief Bioinform"},{"issue":"4","key":"5806_CR24","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1093\/bib\/bbv066","volume":"17","author":"X Chen","year":"2016","unstructured":"Chen X, Yan CC, Zhang X, Zhang X, Dai F, Yin J, Zhang Y. Drug-target interaction prediction: databases, web servers and computational models. Brief Bioinform. 2016;17(4):696\u2013712.","journal-title":"Brief Bioinform"},{"issue":"1","key":"5806_CR25","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1093\/bib\/bbab545","volume":"23","author":"S Pang","year":"2022","unstructured":"Pang S, Zhang Y, Song T, Zhang X, Wang X, Rodriguez-Pat\u00f3n A. AMDE: a novel attention-mechanism-based multidimensional feature encoder for drug\u2013drug interaction prediction. Brief Bioinform. 2022;23(1):545.","journal-title":"Brief Bioinform"},{"issue":"17","key":"5806_CR26","doi-asserted-by":"publisher","first-page":"2651","DOI":"10.1093\/bioinformatics\/btab169","volume":"37","author":"Y Chen","year":"2021","unstructured":"Chen Y, Ma T, Yang X, Wang J, Song B, Zeng X. MUFFIN: multi-scale feature fusion for drug-drug interaction prediction. Bioinformatics. 2021;37(17):2651\u20138.","journal-title":"Bioinformatics"},{"issue":"1","key":"5806_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-022-28494-3","volume":"13","author":"Z Zeng","year":"2022","unstructured":"Zeng Z, Yao Y, Liu Z, Sun M. A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals. Nat Commun. 2022;13(1):1\u201311.","journal-title":"Nat Commun"},{"issue":"16","key":"5806_CR28","doi-asserted-by":"publisher","first-page":"8749","DOI":"10.1021\/acs.jmedchem.9b00959","volume":"63","author":"Z Xiong","year":"2019","unstructured":"Xiong Z, Wang D, Liu X, Zhong F, Wan X, Li X, Li Z, Luo X, Chen K, Jiang H, et al. Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism. J Med Chem. 2019;63(16):8749\u201360.","journal-title":"J Med Chem"},{"issue":"D1","key":"5806_CR29","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1093\/nar\/gku1066","volume":"43","author":"M-C Cai","year":"2015","unstructured":"Cai M-C, Xu Q, Pan Y-J, Pan W, Ji N, Li Y-B, Jin H-J, Liu K, Ji Z-L. ARReCS: an ontology database for aiding standardization and hierarchical classification of adverse drug reaction terms. Nucleic Acids Res. 2015;43(D1):907\u201313.","journal-title":"Nucleic Acids Res"},{"issue":"13","key":"5806_CR30","doi-asserted-by":"publisher","first-page":"1644","DOI":"10.1093\/bioinformatics\/btq241","volume":"26","author":"D Wang","year":"2010","unstructured":"Wang D, Wang J, Lu M, Song F, Cui Q. Inferring the human microrna functional similarity and functional network based on microrna-associated diseases. Bioinformatics. 2010;26(13):1644\u201350.","journal-title":"Bioinformatics"},{"issue":"3","key":"5806_CR31","doi-asserted-by":"publisher","first-page":"232470961772830","DOI":"10.1177\/2324709617728302","volume":"5","author":"U Iqbal","year":"2017","unstructured":"Iqbal U, Siddiqui HU, Anwar H, Chaudhary A, Quadri AA. Allopurinol-induced granulomatous hepatitis: a case report and review of literature. J Investig Med High Impact Case Rep. 2017;5(3):2324709617728302.","journal-title":"J Investig Med High Impact Case Rep"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-024-05806-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-024-05806-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-024-05806-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T07:03:07Z","timestamp":1731394987000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-024-05806-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,20]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["5806"],"URL":"https:\/\/doi.org\/10.1186\/s12859-024-05806-6","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,20]]},"assertion":[{"value":"11 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"196"}}