{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T11:17:20Z","timestamp":1774178240419,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"vor","delay-in-days":59,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["2337425"],"award-info":[{"award-number":["2337425"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["2349486"],"award-info":[{"award-number":["2349486"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,10,12]]},"DOI":"10.1145\/3765612.3767307","type":"proceedings-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T17:45:59Z","timestamp":1765388759000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["MCL-DMD: Multi-modal Contrastive Learning for Drug-Microbe-Disease Association Prediction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0734-1590","authenticated-orcid":false,"given":"Niecia","family":"Say","sequence":"first","affiliation":[{"name":"Georgia Gwinnett College, Atlanta, GA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8526-8159","authenticated-orcid":false,"given":"Farhan","family":"Tanvir","sequence":"additional","affiliation":[{"name":"Georgia State University, Atlanta, GA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6274-7374","authenticated-orcid":false,"given":"Moctar","family":"Keita","sequence":"additional","affiliation":[{"name":"University of Health Sciences and Pharmacy in St. Louis, St. Louis, MO, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5360-4605","authenticated-orcid":false,"given":"Lilia","family":"Chebbah","sequence":"additional","affiliation":[{"name":"Georgia State University, Atlanta, GA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1293-400X","authenticated-orcid":false,"given":"Muhammad Ifte Khairul","family":"Islam","sequence":"additional","affiliation":[{"name":"Georgia State University, Atlanta, GA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8817-2442","authenticated-orcid":false,"given":"Esra","family":"Akbas","sequence":"additional","affiliation":[{"name":"Georgia State University, Atlanta, GA, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,12,10]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Jiawei Luo, and Xiaoli Li. Ensembling graph attention networks for human microbe-drug association prediction. Bioinformatics, 36 Supplement_2:i779\u2013i786","author":"Long Yahui","year":"2020","unstructured":"Yahui Long, Min Wu, Yong Liu, Chee Keong Kwoh, Jiawei Luo, and Xiaoli Li. Ensembling graph attention networks for human microbe-drug association prediction. Bioinformatics, 36 Supplement_2:i779\u2013i786, 2020."},{"key":"e_1_3_2_1_2_1","first-page":"345","volume-title":"Electronics & Mobile Communication Conference (UEMCON)","author":"Rahman Showrov","year":"2023","unstructured":"Showrov Rahman, Yashaswini Mandalam, and Kaushallya Adhikari. Performance analysis of svm-based doa estimation for uniform linear arrays. 2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pages 339\u2013345, 2023."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty294"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2021.3082183"},{"issue":"68","key":"e_1_3_2_1_5_1","first-page":"75","article-title":"A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data","volume":"3","author":"Liu Ruoqi","year":"2021","unstructured":"Ruoqi Liu, Lai Wei, and Ping Zhang. A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data. Nature Machine Intelligence, 3:68 \u2013 75, 2021.","journal-title":"Nature Machine Intelligence"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11427-021-2121-0"},{"key":"e_1_3_2_1_7_1","volume-title":"Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models. Briefings in bioinformatics","author":"Wang Lei","year":"2022","unstructured":"Lei Wang, Yaqin Tan, Xiaoyu Yang, Linai Kuang, and Pengyao Ping. Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models. Briefings in bioinformatics, 2022."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313476"},{"key":"e_1_3_2_1_9_1","volume-title":"Semi-supervised classification with graph convolutional networks. ArXiv, abs\/1609.02907","author":"Kipf Thomas","year":"2016","unstructured":"Thomas Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. ArXiv, abs\/1609.02907, 2016."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/537"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA61799.2024.10722832"},{"key":"e_1_3_2_1_12_1","volume-title":"Vatt: Transformers for multimodal self-supervised learning from raw video, audio and text. ArXiv, abs\/2104.11178","author":"Akbari Hassan","year":"2021","unstructured":"Hassan Akbari, Linagzhe Yuan, Rui Qian, Wei-Hong Chuang, Shih-Fu Chang, Yin Cui, and Boqing Gong. Vatt: Transformers for multimodal self-supervised learning from raw video, audio and text. ArXiv, abs\/2104.11178, 2021."},{"key":"e_1_3_2_1_13_1","volume-title":"International Conference on Machine Learning","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, 2021."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512211"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10953"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"e_1_3_2_1_17_1","volume-title":"Data fusion by matrix factorization","author":"\u017ditnik Marinka","year":"2014","unstructured":"Marinka \u017ditnik and Bla\u017e Zupan. Data fusion by matrix factorization. IEEE transactions on pattern analysis and machine intelligence, 37(1):41\u201353, 2014."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/477"},{"key":"e_1_3_2_1_19_1","first-page":"8","article-title":"A special resource for microbe-drug associations","author":"Sun Ya-Zhou","year":"2018","unstructured":"Ya-Zhou Sun, De-Hong Zhang, Shubin Cai, Zhong Ming, Jianqiang Li, and Xing Chen. Mdad: A special resource for microbe-drug associations. Frontiers in Cellular and Infection Microbiology, 8, 2018.","journal-title":"Frontiers in Cellular and Infection Microbiology"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkx1157"},{"issue":"268","key":"e_1_3_2_1_21_1","first-page":"276","article-title":"Discovery and development of safe-in-man broad-spectrum antiviral agents","volume":"93","author":"Andersen Petter I.","year":"2019","unstructured":"Petter I. Andersen, Aleksandr Ianevski, Hilde Lysvand, Astra Vitkauskien\u0117, Valentyn Oksenych, Magnar Bj\u00f8r\u00e5s, Kaidi Telling, Irja Lutsar, Uga Dumpis, Yasuhiko Irie, Tanel Tenson, Anu Kantele, and Denis E. Kainov. Discovery and development of safe-in-man broad-spectrum antiviral agents. International Journal of Infectious Diseases, 93:268 \u2013 276, 2019.","journal-title":"International Journal of Infectious Diseases"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbw005"},{"key":"e_1_3_2_1_23_1","first-page":"18","article-title":"linking the microbiome to disease","author":"Janssens Yorick","year":"2018","unstructured":"Yorick Janssens, Joachim Nielandt, Antoon Bronselaer, Nathan Debunne, Frederick Verbeke, Evelien Wynendaele, Filip Van Immerseel, Yves-Paul Vandewynckel, Guy de Tr\u00e9, and Bart de Spiegeleer. Disbiome database: linking the microbiome to disease. BMC Microbiology, 18, 2018.","journal-title":"BMC Microbiology"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkz843"},{"key":"e_1_3_2_1_25_1","volume-title":"Peryton: a manual collection of experimentally supported microbe-disease associations. Nucleic acids research, 49(D1):D1328\u2013D1333","author":"Skoufos Giorgos","year":"2021","unstructured":"Giorgos Skoufos, Filippos S Kardaras, Athanasios Alexiou, Ioannis Kavakiotis, Anastasia Lambropoulou, Vasiliki Kotsira, Spyros Tastsoglou, and Artemis G Hatzigeorgiou. Peryton: a manual collection of experimentally supported microbe-disease associations. Nucleic acids research, 49(D1):D1328\u2013D1333, 2021."},{"key":"e_1_3_2_1_26_1","volume-title":"Pubchem substance and compound databases. Nucleic acids research, 44(D1):D1202\u2013D1213","author":"Kim Sunghwan","year":"2016","unstructured":"Sunghwan Kim, Paul A Thiessen, Evan E Bolton, Jie Chen, Gang Fu, Asta Gindulyte, Lianyi Han, Jane He, Siqian He, Benjamin A Shoemaker, et al. Pubchem substance and compound databases. Nucleic acids research, 44(D1):D1202\u2013D1213, 2016."},{"key":"e_1_3_2_1_27_1","volume-title":"The ncbi taxonomy database. Nucleic acids research, 40(D1):D136\u2013D143","author":"Federhen Scott","year":"2012","unstructured":"Scott Federhen. The ncbi taxonomy database. Nucleic acids research, 40(D1):D136\u2013D143, 2012."},{"issue":"3","key":"e_1_3_2_1_28_1","first-page":"265","article-title":"Medical subject headings (mesh)","volume":"88","author":"Lipscomb Carolyn E","year":"2000","unstructured":"Carolyn E Lipscomb. Medical subject headings (mesh). Bulletin of the Medical Library Association, 88(3):265, 2000.","journal-title":"Bulletin of the Medical Library Association"},{"key":"e_1_3_2_1_29_1","volume-title":"NeurIPS Learning Meaningful Representation of Life Workshop","author":"Huang Kexin","year":"2019","unstructured":"Kexin Huang, Cao Xiao, Lucas Glass, and Jimeng Sun. Explainable substructure partition fingerprint for protein, drug, and more. NeurIPS Learning Meaningful Representation of Life Workshop, 2019."},{"key":"e_1_3_2_1_30_1","volume-title":"How powerful are graph neural networks? ArXiv, abs\/1810.00826","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? ArXiv, abs\/1810.00826, 2018."},{"key":"e_1_3_2_1_31_1","volume-title":"Bioinformatics","author":"Ma Yuanjing","year":"2020","unstructured":"Yuanjing Ma and Hongmei Jiang. Ninimhmda: neural integration of neighborhood information on a multiplex heterogeneous network for multiple types of human microbe-disease association. Bioinformatics, 2020."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210017"},{"key":"e_1_3_2_1_33_1","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Liu Hanpeng","year":"2019","unstructured":"Hanpeng Liu, Yaguang Li, Michael Tsang, and Yan Liu. Costco: A neural tensor completion model for sparse tensors. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019."}],"event":{"name":"BCB '25: 16th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","location":"Element Philadelphia Downtown Philadelphia PA USA","acronym":"BCB '25","sponsor":["SIGBio ACM Special Interest Group on Bioinformatics"]},"container-title":["Proceedings of the 16th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3765612.3767307","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3765612.3767307","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T17:49:26Z","timestamp":1765388966000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3765612.3767307"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,12]]},"references-count":33,"alternative-id":["10.1145\/3765612.3767307","10.1145\/3765612"],"URL":"https:\/\/doi.org\/10.1145\/3765612.3767307","relation":{},"subject":[],"published":{"date-parts":[[2025,10,12]]},"assertion":[{"value":"2025-12-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}