{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T05:13:39Z","timestamp":1755839619721,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"US National Institutes of Health","award":["R01AG077016"],"award-info":[{"award-number":["R01AG077016"]}]},{"name":"US National Science Foundation","award":["2238275"],"award-info":[{"award-number":["2238275"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,21]]},"DOI":"10.1145\/3583780.3615490","type":"proceedings-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:45:42Z","timestamp":1697874342000},"page":"4724-4730","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["pADR: Towards Personalized Adverse Drug Reaction Prediction by Modeling Multi-sourced Data"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4897-7051","authenticated-orcid":false,"given":"Junyu","family":"Luo","sequence":"first","affiliation":[{"name":"The Pennsylvania State University, University Park, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2249-4681","authenticated-orcid":false,"given":"Cheng","family":"Qian","sequence":"additional","affiliation":[{"name":"IQVIA, Chicago, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7699-3016","authenticated-orcid":false,"given":"Xiaochen","family":"Wang","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University, University Park, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6613-5205","authenticated-orcid":false,"given":"Lucas","family":"Glass","sequence":"additional","affiliation":[{"name":"IQVIA, Chicago, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4999-0303","authenticated-orcid":false,"given":"Fenglong","family":"Ma","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University, University Park, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1046\/j.1528-1157.43.s.3.5.x"},{"key":"e_1_3_2_1_2_1","volume-title":"The rise of deep learning in drug discovery. Drug discovery today 23, 6","author":"Chen Hongming","year":"2018","unstructured":"Hongming Chen , Ola Engkvist , Yinhai Wang , Marcus Olivecrona , and Thomas Blaschke . 2018. The rise of deep learning in drug discovery. Drug discovery today 23, 6 ( 2018 ), 1241--1250. Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona, and Thomas Blaschke. 2018. The rise of deep learning in drug discovery. Drug discovery today 23, 6 (2018), 1241--1250."},{"key":"e_1_3_2_1_3_1","volume-title":"ChemBERTa: large-scale self-supervised pretraining for molecular property prediction. arXiv preprint arXiv:2010.09885","author":"Chithrananda Seyone","year":"2020","unstructured":"Seyone Chithrananda , Gabriel Grand , and Bharath Ramsundar . 2020. ChemBERTa: large-scale self-supervised pretraining for molecular property prediction. arXiv preprint arXiv:2010.09885 ( 2020 ). Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar. 2020. ChemBERTa: large-scale self-supervised pretraining for molecular property prediction. arXiv preprint arXiv:2010.09885 (2020)."},{"key":"e_1_3_2_1_4_1","volume-title":"Drug-drug adverse effect prediction with graph co-attention. arXiv preprint arXiv:1905.00534","author":"Deac Andreea","year":"2019","unstructured":"Andreea Deac , Yu-Hsiang Huang , Petar Velickovic , Pietro Li\u00f2 , and Jian Tang . 2019. Drug-drug adverse effect prediction with graph co-attention. arXiv preprint arXiv:1905.00534 ( 2019 ). Andreea Deac, Yu-Hsiang Huang, Petar Velickovic, Pietro Li\u00f2, and Jian Tang. 2019. Drug-drug adverse effect prediction with graph co-attention. arXiv preprint arXiv:1905.00534 (2019)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Yu Gu Robert Tinn Hao Cheng Michael Lucas Naoto Usuyama Xiaodong Liu Tristan Naumann Jianfeng Gao and Hoifung Poon. 2020. Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing. arXiv:arXiv:2007.15779  Yu Gu Robert Tinn Hao Cheng Michael Lucas Naoto Usuyama Xiaodong Liu Tristan Naumann Jianfeng Gao and Hoifung Poon. 2020. Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing. arXiv:arXiv:2007.15779","DOI":"10.1145\/3458754"},{"key":"e_1_3_2_1_6_1","volume-title":"Smiles transformer: Pre-trained molecular fingerprint for low data drug discovery. arXiv preprint arXiv:1911.04738","author":"Honda Shion","year":"2019","unstructured":"Shion Honda , Shoi Shi , and Hiroki R Ueda . 2019. Smiles transformer: Pre-trained molecular fingerprint for low data drug discovery. arXiv preprint arXiv:1911.04738 ( 2019 ). Shion Honda, Shoi Shi, and Hiroki R Ueda. 2019. Smiles transformer: Pre-trained molecular fingerprint for low data drug discovery. arXiv preprint arXiv:1911.04738 (2019)."},{"key":"e_1_3_2_1_7_1","volume-title":"Proceedings of NAACL-HLT. 4171--4186","author":"Ming-Wei Chang Jacob Devlin","year":"2019","unstructured":"Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova . 2019 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding . In Proceedings of NAACL-HLT. 4171--4186 . Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT. 4171--4186."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Sunghwan Kim Jie Chen Tiejun Cheng Asta Gindulyte Jia He Siqian He Qingliang Li Benjamin A Shoemaker Paul A Thiessen Bo Yu etal 2019. Pub-Chem 2019 update: improved access to chemical data. Nucleic acids research 47 D1 (2019) D1102--D1109.  Sunghwan Kim Jie Chen Tiejun Cheng Asta Gindulyte Jia He Siqian He Qingliang Li Benjamin A Shoemaker Paul A Thiessen Bo Yu et al. 2019. Pub-Chem 2019 update: improved access to chemical data. Nucleic acids research 47 D1 (2019) D1102--D1109.","DOI":"10.1093\/nar\/gky1033"},{"key":"e_1_3_2_1_9_1","volume-title":"Lars Juhl Jensen, and Peer Bork","author":"Kuhn Michael","year":"2016","unstructured":"Michael Kuhn , Ivica Letunic , Lars Juhl Jensen, and Peer Bork . 2016 . The SIDER database of drugs and side effects. Nucleic acids research 44, D1 (2016), D1075--D1079. Michael Kuhn, Ivica Letunic, Lars Juhl Jensen, and Peer Bork. 2016. The SIDER database of drugs and side effects. Nucleic acids research 44, D1 (2016), D1075--D1079."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0106298"},{"key":"e_1_3_2_1_11_1","volume-title":"Deep learning in drug discovery: opportunities, challenges and future prospects. Drug discovery today 24, 10","author":"Lavecchia Antonio","year":"2019","unstructured":"Antonio Lavecchia . 2019. Deep learning in drug discovery: opportunities, challenges and future prospects. Drug discovery today 24, 10 ( 2019 ), 2017--2032. Antonio Lavecchia. 2019. Deep learning in drug discovery: opportunities, challenges and future prospects. Drug discovery today 24, 10 (2019), 2017--2032."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/int.22389"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2011-000699"},{"key":"e_1_3_2_1_14_1","volume-title":"Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu , Myle Ott , Naman Goyal , Jingfei Du , Mandar Joshi , Danqi Chen , Omer Levy , Mike Lewis , Luke Zettlemoyer , and Veselin Stoyanov . 2019 . Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019). Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.1c01467"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403107"},{"key":"e_1_3_2_1_17_1","volume-title":"Molecule attention transformer. arXiv preprint arXiv:2002.08264","author":"Maziarka Lukasz","year":"2020","unstructured":"Lukasz Maziarka , Tomasz Danel , Slawomir Mucha , Krzysztof Rataj , Jacek Tabor , and Stanislaw Jastrzebski . 2020. Molecule attention transformer. arXiv preprint arXiv:2002.08264 ( 2020 ). Lukasz Maziarka, Tomasz Danel, Slawomir Mucha, Krzysztof Rataj, Jacek Tabor, and Stanislaw Jastrzebski. 2020. Molecule attention transformer. arXiv preprint arXiv:2002.08264 (2020)."},{"key":"e_1_3_2_1_18_1","volume-title":"Ethical issues in clinical research. Perspectives in clinical research 4, 1","author":"Muthuswamy Vasantha","year":"2013","unstructured":"Vasantha Muthuswamy . 2013. Ethical issues in clinical research. Perspectives in clinical research 4, 1 ( 2013 ), 9. Vasantha Muthuswamy. 2013. Ethical issues in clinical research. Perspectives in clinical research 4, 1 (2013), 9."},{"key":"e_1_3_2_1_19_1","volume-title":"et al","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , Luca Antiga , et al . 2019 . Pytorch : An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019), 8026--8037. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al . 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019), 8026--8037."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3097986"},{"key":"e_1_3_2_1_21_1","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel Colin","year":"2020","unstructured":"Colin Raffel , Noam Shazeer , Adam Roberts , Katherine Lee , Sharan Narang , Michael Matena , Yanqi Zhou , Wei Li , Peter J Liu , 2020 . Exploring the limits of transfer learning with a unified text-to-text transformer . J. Mach. Learn. Res. 21 , 140 (2020), 1 -- 67 . Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J Liu, et al. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 140 (2020), 1--67.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_1_22_1","volume-title":"Oznur Tastan, and A Ercument Cicek.","author":"Uner Onur Can","year":"2019","unstructured":"Onur Can Uner , Ramazan Gokberk Cinbis , Oznur Tastan, and A Ercument Cicek. 2019 . DeepSide: a deep learning framework for drug side effect prediction. Biorxiv ( 2019), 843029. Onur Can Uner, Ramazan Gokberk Cinbis, Oznur Tastan, and A Ercument Cicek. 2019. DeepSide: a deep learning framework for drug side effect prediction. Biorxiv (2019), 843029."},{"key":"e_1_3_2_1_23_1","volume-title":"Attention is all you need. Advances in neural information processing systems 30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N Gomez , Lukasz Kaiser , and Illia Polosukhin . 2017. Attention is all you need. Advances in neural information processing systems 30 ( 2017 ). Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_24_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Velickovic Petar","year":"2017","unstructured":"Petar Velickovic , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 ( 2017 ). Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.2196\/11016"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10717"},{"key":"e_1_3_2_1_27_1","volume-title":"One rare side effect of zolpidem-sleepwalking: a case report. Archives of physical medicine and rehabilitation 86, 6","author":"Yang Weibin","year":"2005","unstructured":"Weibin Yang , Mary Dollear , and Sri Ranjini Muthukrishnan . 2005. One rare side effect of zolpidem-sleepwalking: a case report. Archives of physical medicine and rehabilitation 86, 6 ( 2005 ), 1265--1266. Weibin Yang, Mary Dollear, and Sri Ranjini Muthukrishnan. 2005. One rare side effect of zolpidem-sleepwalking: a case report. Archives of physical medicine and rehabilitation 86, 6 (2005), 1265--1266."},{"key":"e_1_3_2_1_28_1","volume-title":"idse-HE: hybrid embedding graph neural network for drug side effects prediction. Journal of Biomedical Informatics","author":"Yu Liyi","year":"2022","unstructured":"Liyi Yu , Meiling Cheng , Wangren Qiu , Xuan Xiao , and Weizhong Lin . 2022. idse-HE: hybrid embedding graph neural network for drug side effects prediction. Journal of Biomedical Informatics ( 2022 ), 104098. Liyi Yu, Meiling Cheng, Wangren Qiu, Xuan Xiao, and Weizhong Lin. 2022. idse-HE: hybrid embedding graph neural network for drug side effects prediction. Journal of Biomedical Informatics (2022), 104098."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119312"}],"event":{"name":"CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Birmingham United Kingdom","acronym":"CIKM '23"},"container-title":["Proceedings of the 32nd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3615490","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3615490","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3615490","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:55Z","timestamp":1750178215000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3615490"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":29,"alternative-id":["10.1145\/3583780.3615490","10.1145\/3583780"],"URL":"https:\/\/doi.org\/10.1145\/3583780.3615490","relation":{},"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}