{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:09:55Z","timestamp":1765544995447,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":67,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Intelligent Social Governance Platform, Major Innovation & Planning Interdisciplinary Platform for the Double-First Class Initiative, Renmin University of China"},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100012226","name":"Research Funds of Renmin University of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Outstanding Young Scientist Program","award":["BJJWZYJH012019100020098"],"award-info":[{"award-number":["BJJWZYJH012019100020098"]}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/100020593","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/100020593","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Outstanding Innovative Talents Cultivation Funded Programs 2023 of Renmin University of China"},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62122089"],"award-info":[{"award-number":["62122089"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,21]]},"DOI":"10.1145\/3627673.3679704","type":"proceedings-article","created":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T19:34:11Z","timestamp":1729452851000},"page":"1856-1866","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Exploiting Pre-trained Models for Drug Target Affinity Prediction with Nearest Neighbors"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7242-422X","authenticated-orcid":false,"given":"Qizhi","family":"Pei","sequence":"first","affiliation":[{"name":"Gaoling School of AI (GSAI), Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3530-590X","authenticated-orcid":false,"given":"Lijun","family":"Wu","sequence":"additional","affiliation":[{"name":"Microsoft Research, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7001-0591","authenticated-orcid":false,"given":"Zhenyu","family":"He","sequence":"additional","affiliation":[{"name":"School of Intelligence Science and Technology, Peking University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2157-9077","authenticated-orcid":false,"given":"Jinhua","family":"Zhu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9823-9033","authenticated-orcid":false,"given":"Yingce","family":"Xia","sequence":"additional","affiliation":[{"name":"Microsoft Research, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7126-0139","authenticated-orcid":false,"given":"Shufang","family":"Xie","sequence":"additional","affiliation":[{"name":"Gaoling School of AI (GSAI), Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3356-6823","authenticated-orcid":false,"given":"Rui","family":"Yan","sequence":"additional","affiliation":[{"name":"Gaoling School of AI (GSAI), Renmin University of China & Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa544"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab275"},{"key":"e_1_3_2_1_3_1","volume-title":"Conference on Probabilistic Graphical Models. PMLR, 25--36","author":"Bolg\u00e1r Bence","year":"2016","unstructured":"Bence Bolg\u00e1r and P\u00e9ter Antal. 2016. Bayesian matrix factorization with non-random missing data using informative Gaussian process priors and soft evidences. In Conference on Probabilistic Graphical Models. PMLR, 25--36."},{"key":"e_1_3_2_1_4_1","unstructured":"Sebastian Borgeaud Arthur Mensch Jordan Hoffmann Trevor Cai Eliza Rutherford Katie Millican George van den Driessche Jean-Baptiste Lespiau Bogdan Damoc Aidan Clark et al. 2021. Improving language models by retrieving from trillions of tokens. arXiv preprint arXiv:2112.04426 (2021)."},{"key":"e_1_3_2_1_5_1","volume-title":"Computational-experimental approach to drug-target interaction mapping: a case study on kinase inhibitors. PLoS computational biology","author":"Cichonska Anna","year":"2017","unstructured":"Anna Cichonska, Balaguru Ravikumar, Elina Parri, Sanna Timonen, Tapio Pahikkala, Antti Airola, Krister Wennerberg, Juho Rousu, and Tero Aittokallio. 2017. Computational-experimental approach to drug-target interaction mapping: a case study on kinase inhibitors. PLoS computational biology, Vol. 13, 8 (2017), e1005678."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btp163"},{"key":"e_1_3_2_1_7_1","volume-title":"Comprehensive analysis of kinase inhibitor selectivity. Nature biotechnology","author":"Davis Mindy I","year":"2011","unstructured":"Mindy I Davis, Jeremy P Hunt, Sanna Herrgard, Pietro Ciceri, Lisa M Wodicka, Gabriel Pallares, Michael Hocker, Daniel K Treiber, and Patrick P Zarrinkar. 2011. Comprehensive analysis of kinase inhibitor selectivity. Nature biotechnology, Vol. 29, 11 (2011), 1046--1051."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbt056"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2020.03.003"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00356"},{"key":"e_1_3_2_1_11_1","volume-title":"UniMAP: Universal SMILES-Graph Representation Learning. arXiv preprint arXiv:2310.14216","author":"Feng Shikun","year":"2023","unstructured":"Shikun Feng, Lixin Yang, Weiying Ma, and Yanyan Lan. 2023. UniMAP: Universal SMILES-Graph Representation Learning. arXiv preprint arXiv:2310.14216 (2023)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1198\/004017002317375064"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.biophys.36.040306.132550"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/92.4.965"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12013"},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Machine Learning. PMLR, 3929--3938","author":"Guu Kelvin","year":"2020","unstructured":"Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020. Retrieval augmented language model pre-training. In International Conference on Machine Learning. PMLR, 3929--3938."},{"key":"e_1_3_2_1_17_1","volume-title":"Realm: Retrieval-augmented language model pre-training. arXiv preprint arXiv:2002.08909","author":"Guu Kelvin","year":"2020","unstructured":"Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020. Realm: Retrieval-augmented language model pre-training. arXiv preprint arXiv:2002.08909 (2020)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-017-0209-z"},{"key":"e_1_3_2_1_19_1","volume-title":"Long short-term memory. Neural computation","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780."},{"key":"e_1_3_2_1_20_1","first-page":"22","article-title":"DeepPurpose: a deep learning library for drug--target interaction prediction","volume":"36","author":"Huang Kexin","year":"2020","unstructured":"Kexin Huang, Tianfan Fu, Lucas M Glass, Marinka Zitnik, Cao Xiao, and Jimeng Sun. 2020. DeepPurpose: a deep learning library for drug--target interaction prediction. Bioinformatics, Vol. 36, 22--23 (2020), 5545--5547.","journal-title":"Bioinformatics"},{"volume-title":"Wiley Encyclopedia of Chemical Biology (2007), 1--15.","author":"Inglese James","key":"e_1_3_2_1_21_1","unstructured":"James Inglese and Douglas S Auld. 2007. High throughput screening (HTS) techniques: applications in chemical biology. Wiley Encyclopedia of Chemical Biology (2007), 1--15."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","first-page":"e0246920","DOI":"10.1371\/journal.pone.0246920","article-title":"DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation","volume":"16","author":"Islam Sk Mazharul","year":"2021","unstructured":"Sk Mazharul Islam, Sk Md Mosaddek Hossain, and Sumanta Ray. 2021. DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation. Plos one, Vol. 16, 2 (2021), e0246920.","journal-title":"Plos one"},{"key":"e_1_3_2_1_23_1","volume-title":"Sequence-based drug-target affinity prediction using weighted graph neural networks. BMC genomics","author":"Jiang Mingjian","year":"2022","unstructured":"Mingjian Jiang, Shuang Wang, Shugang Zhang, Wei Zhou, Yuanyuan Zhang, and Zhen Li. 2022. Sequence-based drug-target affinity prediction using weighted graph neural networks. BMC genomics, Vol. 23, 1 (2022), 1--17."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2019.2921572"},{"key":"e_1_3_2_1_25_1","volume-title":"Learning to remember rare events. arXiv preprint arXiv:1703.03129","author":"Kaiser \u0141ukasz","year":"2017","unstructured":"\u0141ukasz Kaiser, Ofir Nachum, Aurko Roy, and Samy Bengio. 2017. Learning to remember rare events. arXiv preprint arXiv:1703.03129 (2017)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz111"},{"key":"e_1_3_2_1_27_1","volume-title":"Nearest Neighbor Machine Translation. In International Conference on Learning Representations.","author":"Khandelwal Urvashi","year":"2020","unstructured":"Urvashi Khandelwal, Angela Fan, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2020. Nearest Neighbor Machine Translation. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_28_1","volume-title":"International Conference on Learning Representations.","author":"Khandelwal Urvashi","year":"2019","unstructured":"Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2019. Generalization through Memorization: Nearest Neighbor Language Models. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_29_1","unstructured":"Greg Landrum et al. 2013. RDKit: A software suite for cheminformatics computational chemistry and predictive modeling. Greg Landrum (2013)."},{"key":"e_1_3_2_1_30_1","first-page":"9459","article-title":"Retrieval-augmented generation for knowledge-intensive nlp tasks","volume":"33","author":"Lewis Patrick","year":"2020","unstructured":"Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich K\u00fcttler, Mike Lewis, Wen-tau Yih, Tim Rockt\u00e4schel, et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, Vol. 33 (2020), 9459--9474.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_31_1","volume-title":"BACPI: a bi-directional attention neural network for compound-protein interaction and binding affinity prediction. Bioinformatics","author":"Li Min","year":"2022","unstructured":"Min Li, Zhangli Lu, Yifan Wu, and YaoHang Li. 2022. BACPI: a bi-directional attention neural network for compound-protein interaction and binding affinity prediction. Bioinformatics (2022)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cels.2020.03.002"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02495-z"},{"key":"e_1_3_2_1_34_1","volume-title":"BindingDB: a web-accessible database of experimentally determined protein--ligand binding affinities. Nucleic acids research","author":"Liu Tiqing","year":"2007","unstructured":"Tiqing Liu, Yuhmei Lin, Xin Wen, Robert N Jorissen, and Michael K Gilson. 2007. BindingDB: a web-accessible database of experimentally determined protein--ligand binding affinities. Nucleic acids research, Vol. 35, suppl_1 (2007), D198--D201."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa921"},{"key":"e_1_3_2_1_36_1","volume-title":"Thao Minh Le, and Truyen Tran","author":"Nguyen Tri Minh","year":"2022","unstructured":"Tri Minh Nguyen, Thin Nguyen, Thao Minh Le, and Truyen Tran. 2022. GEFA: Early Fusion Approach in Drug-Target Affinity Prediction. IEEE\/ACM transactions on computational biology and bioinformatics, Vol. 19, 2 (2022), 718--728."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty593"},{"key":"e_1_3_2_1_38_1","volume-title":"WideDTA: prediction of drug-target binding affinity. arXiv preprint arXiv:1902.04166","author":"\u00d6zt\u00fcrk Hakime","year":"2019","unstructured":"Hakime \u00d6zt\u00fcrk, Elif Ozkirimli, and Arzucan \u00d6zg\u00fcr. 2019. WideDTA: prediction of drug-target binding affinity. arXiv preprint arXiv:1902.04166 (2019)."},{"key":"e_1_3_2_1_39_1","volume-title":"Toward more realistic drug--target interaction predictions. Briefings in bioinformatics","author":"Pahikkala Tapio","year":"2015","unstructured":"Tapio Pahikkala, Antti Airola, Sami Pietil\u00e4, Sushil Shakyawar, Agnieszka Szwajda, Jing Tang, and Tero Aittokallio. 2015. Toward more realistic drug--target interaction predictions. Briefings in bioinformatics, Vol. 16, 2 (2015), 325--337."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad386"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Sudeep Pushpakom Francesco Iorio Patrick A Eyers K Jane Escott Shirley Hopper Andrew Wells Andrew Doig Tim Guilliams Joanna Latimer Christine McNamee et al. 2019. Drug repurposing: progress challenges and recommendations. Nature reviews Drug discovery Vol. 18 1 (2019) 41--58.","DOI":"10.1038\/nrd.2018.168"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btac048"},{"key":"e_1_3_2_1_43_1","volume-title":"Molecular dynamics simulations of protein dynamics and their relevance to drug discovery. Current opinion in pharmacology","author":"Salsbury Freddie R","year":"2010","unstructured":"Freddie R Salsbury Jr. 2010. Molecular dynamics simulations of protein dynamics and their relevance to drug discovery. Current opinion in pharmacology, Vol. 10, 6 (2010), 738--744."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.3109\/14756366.2016.1144594"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.2174\/1389203718666161108093907"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-83679-y"},{"key":"e_1_3_2_1_47_1","unstructured":"Sainbayar Sukhbaatar Jason Weston Rob Fergus et al. 2015. End-to-end memory networks. Advances in neural information processing systems Vol. 28 (2015)."},{"key":"e_1_3_2_1_48_1","volume-title":"iEdgeDTA: integrated edge information and 1D graph convolutional neural networks for binding affinity prediction. RSC advances","author":"Suviriyapaisal Natchanon","year":"2023","unstructured":"Natchanon Suviriyapaisal and Duangdao Wichadakul. 2023. iEdgeDTA: integrated edge information and 1D graph convolutional neural networks for binding affinity prediction. RSC advances, Vol. 13, 36 (2023), 25218--25228."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci400709d"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115810"},{"key":"e_1_3_2_1_51_1","volume-title":"Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning. Scientific reports","author":"Thafar Maha A","year":"2022","unstructured":"Maha A Thafar, Mona Alshahrani, Somayah Albaradei, Takashi Gojobori, Magbubah Essack, and Xin Gao. 2022. Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning. Scientific reports, Vol. 12, 1 (2022), 1--18."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-021-00552-w"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1002\/jcc.21334"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0066952"},{"key":"e_1_3_2_1_55_1","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1002\/prot.10465"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci00057a005"},{"key":"e_1_3_2_1_58_1","volume-title":"3rd International Conference on Learning Representations, ICLR","author":"Weston Jason","year":"2015","unstructured":"Jason Weston, Sumit Chopra, and Antoine Bordes. 2015. Memory networks. In 3rd International Conference on Learning Representations, ICLR 2015."},{"key":"e_1_3_2_1_59_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Xia Jun","year":"2022","unstructured":"Jun Xia, Chengshuai Zhao, Bozhen Hu, Zhangyang Gao, Cheng Tan, Yue Liu, Siyuan Li, and Stan Z Li. 2022. Mole-bert: Rethinking pre-training graph neural networks for molecules. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25664"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btn162"},{"key":"e_1_3_2_1_62_1","volume-title":"MGraphDTA: deep multiscale graph neural network for explainable drug--target binding affinity prediction. Chemical science","author":"Yang Ziduo","year":"2022","unstructured":"Ziduo Yang, Weihe Zhong, Lu Zhao, and Calvin Yu-Chian Chen. 2022. MGraphDTA: deep multiscale graph neural network for explainable drug--target binding affinity prediction. Chemical science, Vol. 13, 3 (2022), 816--833."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.301"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052580"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106110"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-short.47"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(20)30192-8"}],"event":{"name":"CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Boise ID USA","acronym":"CIKM '24"},"container-title":["Proceedings of the 33rd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679704","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627673.3679704","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:58:13Z","timestamp":1750294693000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679704"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":67,"alternative-id":["10.1145\/3627673.3679704","10.1145\/3627673"],"URL":"https:\/\/doi.org\/10.1145\/3627673.3679704","relation":{},"subject":[],"published":{"date-parts":[[2024,10,21]]},"assertion":[{"value":"2024-10-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}