{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T19:09:24Z","timestamp":1772824164393,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":63,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001381","name":"National Research Foundation Singapore","doi-asserted-by":"publisher","award":["AISG-100E-2023-116"],"award-info":[{"award-number":["AISG-100E-2023-116"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001352","name":"National University of Singapore","doi-asserted-by":"publisher","id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001352","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100020576","name":"Cancer Science Institute of Singapore, National University of Singapore","doi-asserted-by":"publisher","id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100020576","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Pangestu Family Foundation Gynaecological Cancer Research Fund"},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001349","name":"National Medical Research Council","doi-asserted-by":"publisher","award":["MOH-CSAINV20nov-0010,CSASI21jun-0003"],"award-info":[{"award-number":["MOH-CSAINV20nov-0010,CSASI21jun-0003"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001349","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671652","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:55:12Z","timestamp":1724561712000},"page":"5138-5149","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5658-0724","authenticated-orcid":false,"given":"Aishwarya","family":"Jayagopal","sequence":"first","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5263-4844","authenticated-orcid":false,"given":"Hansheng","family":"Xue","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4760-2444","authenticated-orcid":false,"given":"Ziyang","family":"He","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0985-606X","authenticated-orcid":false,"given":"Robert J.","family":"Walsh","sequence":"additional","affiliation":[{"name":"National University Cancer Institute, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2923-6479","authenticated-orcid":false,"given":"Krishna Kumar","family":"Hariprasannan","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9087-5262","authenticated-orcid":false,"given":"David Shao Peng","family":"Tan","sequence":"additional","affiliation":[{"name":"Cancer Science Institute of Singapore, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6624-1593","authenticated-orcid":false,"given":"Tuan Zea","family":"Tan","sequence":"additional","affiliation":[{"name":"Cancer Science Institute of Singapore, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7534-4516","authenticated-orcid":false,"given":"Jason J.","family":"Pitt","sequence":"additional","affiliation":[{"name":"Cancer Science Institute of Singapore, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9816-6137","authenticated-orcid":false,"given":"Anand D.","family":"Jeyasekharan","sequence":"additional","affiliation":[{"name":"Cancer Science Institute of Singapore, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6748-6864","authenticated-orcid":false,"given":"Vaibhav","family":"Rajan","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","first-page":"8072","article-title":"A near-optimal algorithm for debiasing trained machine learning models","volume":"34","author":"Alabdulmohsin Ibrahim M","year":"2021","unstructured":"Ibrahim M Alabdulmohsin and Mario Lucic. 2021. A near-optimal algorithm for debiasing trained machine learning models. Advances in Neural Information Processing Systems, Vol. 34 (2021), 8072--8084.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms140919257"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2007.11.8836"},{"key":"e_1_3_2_2_4_1","volume-title":"B\u00fclent Arman Aksoy, Anders Jacobsen, Caitlin J Byrne, Michael L Heuer, Erik Larsson, et al.","author":"Cerami Ethan","year":"2012","unstructured":"Ethan Cerami, Jianjiong Gao, Ugur Dogrusoz, Benjamin E Gross, Selcuk Onur Sumer, B\u00fclent Arman Aksoy, Anders Jacobsen, Caitlin J Byrne, Michael L Heuer, Erik Larsson, et al. 2012. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer discovery, Vol. 2, 5 (2012), 401--404."},{"key":"e_1_3_2_2_5_1","volume-title":"Hira Rizvi, Kathryn C Arbour, Karissa Whiting, Ronglai Shen, et al.","author":"Choudhury Noura J","year":"2023","unstructured":"Noura J Choudhury, Jessica A Lavery, Samantha Brown, Ino de Bruijn, Justin Jee, Thinh Ngoc Tran, Hira Rizvi, Kathryn C Arbour, Karissa Whiting, Ronglai Shen, et al. 2023. The GENIE BPC NSCLC Cohort: A Real-World Repository Integrating Standardized Clinical and Genomic Data for 1,846 Patients with Non--Small Cell Lung Cancer. Clinical Cancer Research, Vol. 29, 17 (2023), 3418--3428."},{"key":"e_1_3_2_2_6_1","volume-title":"Neural Network-based Treatment Decision Support Tool in Patients With Refractory Solid Organ Malignancies (DRUID). https:\/\/clinicaltrials.gov\/study\/NCT05719428 Retrieved","year":"2024","unstructured":"ClinicalTrials.gov. 2023. Neural Network-based Treatment Decision Support Tool in Patients With Refractory Solid Organ Malignancies (DRUID). https:\/\/clinicaltrials.gov\/study\/NCT05719428 Retrieved February 7, 2024 from"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ctrv.2022.102498"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1972.tb00899.x"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41698-024-00517-w"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3561048"},{"key":"e_1_3_2_2_11_1","volume-title":"Deep neural networks for survival analysis based on a multi-task framework. arXiv preprint arXiv:1801.05512","author":"Fotso Stephane","year":"2018","unstructured":"Stephane Fotso. 2018. Deep neural networks for survival analysis based on a multi-task framework. arXiv preprint arXiv:1801.05512 (2018)."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.6257"},{"key":"e_1_3_2_2_13_1","volume-title":"Jordi Barretina, Ellen T Gelfand, Craig M Bielski, Haoxin Li, et al.","author":"Ghandi Mahmoud","year":"2019","unstructured":"Mahmoud Ghandi, Franklin W Huang, Judit Jan\u00e9-Valbuena, Gregory V Kryukov, Christopher C Lo, E Robert McDonald III, Jordi Barretina, Ellen T Gelfand, Craig M Bielski, Haoxin Li, et al. 2019. Next-generation characterization of the cancer cell line encyclopedia. Nature, Vol. 569, 7757 (2019), 503--508."},{"key":"e_1_3_2_2_14_1","volume-title":"Brian Hart and Nikolai Sellereite","author":"Haavard Kvamme Sarthak Pati","year":"2022","unstructured":"Sarthak Pati Haavard Kvamme, Brian Hart and Nikolai Sellereite. 2022. Survival analysis with PyTorch. https:\/\/github.com\/havakv\/pycox Retrieved February 8, 2024 from"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-022-00541-0"},{"key":"e_1_3_2_2_16_1","volume-title":"Ragunathan Mariappan, Debabrata Mahapatra, Patrick William Jaynes, Diana Lim, David Shao Peng Tan, Tuan Zea Tan, Jason J Pitt, et al.","author":"Jayagopal Aishwarya","year":"2023","unstructured":"Aishwarya Jayagopal, Robert J Walsh, Krishna Kumar Hariprasannan, Ragunathan Mariappan, Debabrata Mahapatra, Patrick William Jaynes, Diana Lim, David Shao Peng Tan, Tuan Zea Tan, Jason J Pitt, et al. 2023. A multi-task domain-adapted model to predict chemotherapy response from mutations in recurrently altered cancer genes. medRxiv (2023), 2023--11."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab083"},{"key":"e_1_3_2_2_18_1","volume-title":"Deep generative neural network for accurate drug response imputation. Nature communications","author":"Jia Peilin","year":"2021","unstructured":"Peilin Jia, Ruifeng Hu, Guangsheng Pei, Yulin Dai, Yin-Ying Wang, and Zhongming Zhao. 2021. Deep generative neural network for accurate drug response imputation. Nature communications, Vol. 12, 1 (2021), 1740."},{"key":"e_1_3_2_2_19_1","volume-title":"DeepTTA: a transformer-based model for predicting cancer drug response. Briefings in bioinformatics","author":"Jiang Likun","year":"2022","unstructured":"Likun Jiang, Changzhi Jiang, Xinyu Yu, Rao Fu, Shuting Jin, and Xiangrong Liu. 2022. DeepTTA: a transformer-based model for predicting cancer drug response. Briefings in bioinformatics, Vol. 23, 3 (2022), bbac100."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1958.10501452"},{"key":"e_1_3_2_2_21_1","volume-title":"DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology","author":"Katzman Jared L","year":"2018","unstructured":"Jared L Katzman, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, and Yuval Kluger. 2018. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology, Vol. 18, 1 (2018), 1--12."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1093\/jjco\/hyw018"},{"key":"e_1_3_2_2_23_1","volume-title":"Continuous and discrete-time survival prediction with neural networks. arXiv preprint arXiv:1910.06724","author":"Kvamme H\u00e5vard","year":"2019","unstructured":"H\u00e5vard Kvamme and \u00d8rnulf Borgan. 2019. Continuous and discrete-time survival prediction with neural networks. arXiv preprint arXiv:1910.06724 (2019)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"crossref","unstructured":"Melissa J Landrum Jennifer M Lee Mark Benson Garth R Brown Chen Chao Shanmuga Chitipiralla Baoshan Gu Jennifer Hart Douglas Hoffman Wonhee Jang et al. 2018. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic acids research Vol. 46 D1 (2018) D1062--D1067.","DOI":"10.1093\/nar\/gkx1153"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11842"},{"key":"e_1_3_2_2_26_1","volume-title":"Review of statistical methods for survival analysis using genomic data. Genomics & informatics","author":"Lee Seungyeoun","year":"2019","unstructured":"Seungyeoun Lee and Heeju Lim. 2019. Review of statistical methods for survival analysis using genomic data. Genomics & informatics, Vol. 17, 4 (2019)."},{"key":"e_1_3_2_2_27_1","volume-title":"A protein-centric approach for exome variant aggregation enables sensitive association analysis with clinical outcomes. Human mutation","author":"Li Ginny XH","year":"2020","unstructured":"Ginny XH Li, Dan Munro, Damian Fermin, Christine Vogel, and Hyungwon Choi. 2020. A protein-centric approach for exome variant aggregation enables sensitive association analysis with clinical outcomes. Human mutation, Vol. 41, 5 (2020), 934--945."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trecan.2020.05.008"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1038\/s43018-020-00169-2"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0264138"},{"key":"e_1_3_2_2_31_1","volume-title":"Collaborative strategies for deploying AI-based physician decision support systems: challenges and deployment approaches. npj Digital Medicine","author":"Mittermaier Mirja","year":"2023","unstructured":"Mirja Mittermaier, Marium Raza, and Joseph C Kvedar. 2023. Collaborative strategies for deploying AI-based physician decision support systems: challenges and deployment approaches. npj Digital Medicine, Vol. 6, 1 (2023), 137."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1021\/c160017a018"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz372"},{"key":"e_1_3_2_2_34_1","volume-title":"Hyenadna: Long-range genomic sequence modeling at single nucleotide resolution. Advances in neural information processing systems","author":"Nguyen Eric","year":"2024","unstructured":"Eric Nguyen, Michael Poli, Marjan Faizi, Armin Thomas, Michael Wornow, Callum Birch-Sykes, Stefano Massaroli, Aman Patel, Clayton Rabideau, Yoshua Bengio, et al. 2024. Hyenadna: Long-range genomic sequence modeling at single nucleotide resolution. Advances in neural information processing systems, Vol. 36 (2024)."},{"key":"e_1_3_2_2_35_1","volume-title":"Jamie Overbeek, and Rick L Stevens.","author":"Partin Alexander","year":"2023","unstructured":"Alexander Partin, Thomas S Brettin, Yitan Zhu, Oleksandr Narykov, Austin Clyde, Jamie Overbeek, and Rick L Stevens. 2023. Deep learning methods for drug response prediction in cancer: predominant and emerging trends. Frontiers in medicine, Vol. 10 (2023), 1086097."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab299"},{"key":"e_1_3_2_2_37_1","volume-title":"H\u00e9ctor Tejero, Takeshi Shimamura, Pedro Pablo L\u00f3pez-Casas, Juli\u00e1n Carretero, et al.","author":"Elena Pi","year":"2018","unstructured":"Elena Pi neiro-Y\u00e1 nez, Miguel Reboiro-Jato, Gonzalo G\u00f3mez-L\u00f3pez, Javier Perales-Pat\u00f3n, Kevin Troul\u00e9, Jos\u00e9 Manuel Rodr\u00edguez, H\u00e9ctor Tejero, Takeshi Shimamura, Pedro Pablo L\u00f3pez-Casas, Juli\u00e1n Carretero, et al. 2018. PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data. Genome medicine, Vol. 10 (2018), 1--11."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1093\/annonc\/mdy275"},{"key":"e_1_3_2_2_39_1","volume-title":"Joanna Patrycja Wroblewska, Petr V Nazarov, Michel Mittelbronn, Katrin BM Frauenknecht, et al.","author":"Randic Tijana","year":"2023","unstructured":"Tijana Randic, Stefano Magni, Demetra Philippidou, Christiane Margue, Kamil Grzyb, Jasmin Renate Preis, Joanna Patrycja Wroblewska, Petr V Nazarov, Michel Mittelbronn, Katrin BM Frauenknecht, et al. 2023. Single-cell transcriptomics of NRAS-mutated melanoma transitioning to drug resistance reveals P2RX7 as an indicator of early drug response. Cell Reports, Vol. 42, 7 (2023)."},{"key":"e_1_3_2_2_40_1","volume-title":"Intra-processing methods for debiasing neural networks. Advances in neural information processing systems","author":"Savani Yash","year":"2020","unstructured":"Yash Savani, Colin White, and Naveen Sundar Govindarajulu. 2020. Intra-processing methods for debiasing neural networks. Advances in neural information processing systems, Vol. 33 (2020), 2798--2810."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.07.018"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00408-w"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa442"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00992-8"},{"key":"e_1_3_2_2_45_1","volume-title":"On ranking in survival analysis: Bounds on the concordance index. Advances in neural information processing systems","author":"Steck Harald","year":"2007","unstructured":"Harald Steck, Balaji Krishnapuram, Cary Dehing-Oberije, Philippe Lambin, and Vikas C Raykar. 2007. On ranking in survival analysis: Bounds on the concordance index. Advances in neural information processing systems, Vol. 20 (2007)."},{"key":"e_1_3_2_2_46_1","volume-title":"Jorrit Boekel, Adria Lopez-Fernandez, Markus Jonsson, Ali Razzak, Irene Bra na, Luigi De Petris, Jeffrey Yachnin, et al.","author":"Tamborero David","year":"2022","unstructured":"David Tamborero, Rodrigo Dienstmann, Maan Haj Rachid, Jorrit Boekel, Adria Lopez-Fernandez, Markus Jonsson, Ali Razzak, Irene Bra na, Luigi De Petris, Jeffrey Yachnin, et al. 2022. The Molecular Tumor Board Portal supports clinical decisions and automated reporting for precision oncology. Nature cancer, Vol. 3, 2 (2022), 251--261."},{"key":"e_1_3_2_2_47_1","volume-title":"PACIFIC SYMPOSIUM ON BIOCOMPUTING","author":"Tao Yifeng","year":"2020","unstructured":"Yifeng Tao, Chunhui Cai, William W Cohen, and Xinghua Lu. 2019. From genome to phenome: Predicting multiple cancer phenotypes based on somatic genomic alterations via the genomic impact transformer. In PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020. World Scientific, 79--90."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/92.3.205"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41571-023-00824-4"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1093\/annonc\/mdq222"},{"key":"e_1_3_2_2_51_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_2_52_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41568-022-00529-3"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1200\/CCI.22.00184"},{"key":"e_1_3_2_2_54_1","volume-title":"ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic acids research","author":"Wang Kai","year":"2010","unstructured":"Kai Wang, Mingyao Li, and Hakon Hakonarson. 2010. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic acids research, Vol. 38, 16 (2010), e164--e164."},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3214306"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmoldx.2022.01.008"},{"key":"e_1_3_2_2_57_1","volume-title":"The cancer genome atlas pan-cancer analysis project. Nature genetics","author":"Weinstein John N","year":"2013","unstructured":"John N Weinstein, Eric A Collisson, Gordon B Mills, Kenna R Shaw, Brad A Ozenberger, Kyle Ellrott, Ilya Shmulevich, Chris Sander, and Joshua M Stuart. 2013. The cancer genome atlas pan-cancer analysis project. Nature genetics, Vol. 45, 10 (2013), 1113--1120."},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"crossref","unstructured":"Jenna Wiens Suchi Saria Mark Sendak Marzyeh Ghassemi Vincent X Liu Finale Doshi-Velez Kenneth Jung Katherine Heller David Kale Mohammed Saeed et al. 2019. Do no harm: a roadmap for responsible machine learning for health care. Nature medicine Vol. 25 9 (2019) 1337--1340.","DOI":"10.1038\/s41591-019-0548-6"},{"key":"e_1_3_2_2_59_1","volume-title":"Matthew R Sydes, Ian F Tannock, et al.","author":"Wilson Michelle K","year":"2015","unstructured":"Michelle K Wilson, Deborah Collyar, Diana T Chingos, Michael Friedlander, Tony W Ho, Katherine Karakasis, Stan Kaye, Mahesh KB Parmar, Matthew R Sydes, Ian F Tannock, et al. 2015. Outcomes and endpoints in cancer trials: bridging the divide. The lancet oncology, Vol. 16, 1 (2015), e43--e52."},{"key":"e_1_3_2_2_60_1","volume-title":"Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang.","author":"Xu Jie","year":"2022","unstructured":"Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A Shenkman, Jiang Bian, and Fei Wang. 2022. Algorithmic fairness in computational medicine. EBioMedicine, Vol. 84 (2022)."},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"crossref","unstructured":"Wanjuan Yang Jorge Soares Patricia Greninger Elena J Edelman Howard Lightfoot Simon Forbes Nidhi Bindal Dave Beare James A Smith I Richard Thompson et al. 2012. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic acids research Vol. 41 D1 (2012) D955--D961.","DOI":"10.1093\/nar\/gks1111"},{"key":"e_1_3_2_2_62_1","volume-title":"Learning patient-specific cancer survival distributions as a sequence of dependent regressors. Advances in neural information processing systems","author":"Yu Chun-Nam","year":"2011","unstructured":"Chun-Nam Yu, Russell Greiner, Hsiu-Chin Lin, and Vickie Baracos. 2011. Learning patient-specific cancer survival distributions as a sequence of dependent regressors. Advances in neural information processing systems, Vol. 24 (2011)."},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"crossref","unstructured":"Ahmet Zehir Ryma Benayed Ronak H Shah Aijazuddin Syed Sumit Middha Hyunjae R Kim Preethi Srinivasan Jianjiong Gao Debyani Chakravarty Sean M Devlin et al. 2017. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10 000 patients. Nature medicine Vol. 23 6 (2017) 703--713.","DOI":"10.1038\/nm.4333"}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671652","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671652","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:06:00Z","timestamp":1750291560000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671652"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":63,"alternative-id":["10.1145\/3637528.3671652","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671652","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}