{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T22:16:33Z","timestamp":1767651393095,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,9,3]],"date-time":"2023-09-03T00:00:00Z","timestamp":1693699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Center for Advancing Translational Science of the National Institutes of Health","award":["U01TR002062"],"award-info":[{"award-number":["U01TR002062"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,9,3]]},"DOI":"10.1145\/3584371.3612967","type":"proceedings-article","created":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T18:52:30Z","timestamp":1696445550000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["MLGAN: a Meta-Learning based Generative Adversarial Network adapter for rare disease differentiation tasks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7292-4011","authenticated-orcid":false,"given":"Rui","family":"Li","sequence":"first","affiliation":[{"name":"State University of New York at Buffalo, Buffalo, NY, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9090-8028","authenticated-orcid":false,"given":"Andrew","family":"Wen","sequence":"additional","affiliation":[{"name":"The University of Texas Health Science Center at Houston, Houston, TX, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1557-7553","authenticated-orcid":false,"given":"Jing","family":"Gao","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2570-3741","authenticated-orcid":false,"given":"Hongfang","family":"Liu","sequence":"additional","affiliation":[{"name":"The University of Texas Health Science Center at Houston, Houston, TX, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,10,4]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Learning to learn by gradient descent by gradient descent. Advances in neural information processing systems 29","author":"Andrychowicz Marcin","year":"2016","unstructured":"Marcin Andrychowicz , Misha Denil , Sergio Gomez , Matthew W Hoffman , David Pfau , Tom Schaul , Brendan Shillingford , and Nando De Freitas . 2016. Learning to learn by gradient descent by gradient descent. Advances in neural information processing systems 29 ( 2016 ). Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas. 2016. Learning to learn by gradient descent by gradient descent. Advances in neural information processing systems 29 (2016)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3097997"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-020-05877-5"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"e_1_3_2_1_5_1","volume-title":"Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart.","author":"Choi Edward","year":"2016","unstructured":"Edward Choi , Mohammad Taha Bahadori , Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart. 2016 . RETAIN : An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism. In Advances in Neural Information Processing Systems, D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett (Eds.), Vol. 29 . Curran Associates, Inc . Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart. 2016. RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism. In Advances in Neural Information Processing Systems, D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett (Eds.), Vol. 29. Curran Associates, Inc."},{"key":"e_1_3_2_1_6_1","unstructured":"Edward Choi Siddharth Biswal Bradley Malin Jon Duke Walter F Stewart and Jimeng Sun. 2017. Generating multi-label discrete patient records using generative adversarial networks. In Machine learning for healthcare conference. PMLR 286--305. Edward Choi Siddharth Biswal Bradley Malin Jon Duke Walter F Stewart and Jimeng Sun. 2017. Generating multi-label discrete patient records using generative adversarial networks. In Machine learning for healthcare conference. PMLR 286--305."},{"key":"e_1_3_2_1_7_1","volume-title":"NeurIPS 2014 Workshop on Deep Learning","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung , Caglar Gulcehre , KyungHyun Cho , and Yoshua Bengio . 2014 . Empirical evaluation of gated recurrent neural networks on sequence modeling . NeurIPS 2014 Workshop on Deep Learning (2014). Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. NeurIPS 2014 Workshop on Deep Learning (2014)."},{"key":"e_1_3_2_1_8_1","unstructured":"Rare Kidney Stone Consortium. 2015. Rare Kidney Stone Consortium. (2015). https:\/\/www.rarekidneystones.org\/ Rare Kidney Stone Consortium. 2015. Rare Kidney Stone Consortium. (2015). https:\/\/www.rarekidneystones.org\/"},{"key":"e_1_3_2_1_9_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"Cui Limeng","year":"2020","unstructured":"Limeng Cui , Siddharth Biswal , Lucas M Glass , Greg Lever , Jimeng Sun , and Cao Xiao . 2020 . CONAN: complementary pattern augmentation for rare disease detection . In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 34 . 614--621. Limeng Cui, Siddharth Biswal, Lucas M Glass, Greg Lever, Jimeng Sun, and Cao Xiao. 2020. CONAN: complementary pattern augmentation for rare disease detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 614--621."},{"key":"e_1_3_2_1_10_1","volume-title":"Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision. arXiv preprint arXiv:2209.02459","author":"Dorigatti Emilio","year":"2022","unstructured":"Emilio Dorigatti , Jonas Schweisthal , Bernd Bischl , and Mina Rezaei . 2022. Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision. arXiv preprint arXiv:2209.02459 ( 2022 ). Emilio Dorigatti, Jonas Schweisthal, Bernd Bischl, and Mina Rezaei. 2022. Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision. arXiv preprint arXiv:2209.02459 (2022)."},{"key":"e_1_3_2_1_11_1","volume-title":"International conference on machine learning. PMLR, 1386--1394","author":"Plessis Marthinus Du","year":"2015","unstructured":"Marthinus Du Plessis , Gang Niu , and Masashi Sugiyama . 2015 . Convex formulation for learning from positive and unlabeled data . In International conference on machine learning. PMLR, 1386--1394 . Marthinus Du Plessis, Gang Niu, and Masashi Sugiyama. 2015. Convex formulation for learning from positive and unlabeled data. In International conference on machine learning. PMLR, 1386--1394."},{"key":"e_1_3_2_1_12_1","volume-title":"Analysis of learning from positive and unlabeled data. Advances in neural information processing systems 27","author":"Du Plessis Marthinus C","year":"2014","unstructured":"Marthinus C Du Plessis , Gang Niu , and Masashi Sugiyama . 2014. Analysis of learning from positive and unlabeled data. Advances in neural information processing systems 27 ( 2014 ). Marthinus C Du Plessis, Gang Niu, and Masashi Sugiyama. 2014. Analysis of learning from positive and unlabeled data. Advances in neural information processing systems 27 (2014)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401920"},{"key":"e_1_3_2_1_14_1","volume-title":"Survey of the delay in diagnosis for 8 rare diseases in Europe (EurordisCare2). Fact sheet EurordisCare 2","author":"EURORDIS.","year":"2007","unstructured":"EURORDIS. 2007. Survey of the delay in diagnosis for 8 rare diseases in Europe (EurordisCare2). Fact sheet EurordisCare 2 ( 2007 ). EURORDIS. 2007. Survey of the delay in diagnosis for 8 rare diseases in Europe (EurordisCare2). Fact sheet EurordisCare 2 (2007)."},{"key":"e_1_3_2_1_15_1","unstructured":"Centers for Medicare Medicaid Services and the National Center for Health Statistics. 2018. Diagnosis Code Set General Equivalence Mappings. (2018). https:\/\/www.cms.gov\/medicare\/coding\/icd10\/2018-icd-10-cm-and-gems Centers for Medicare Medicaid Services and the National Center for Health Statistics. 2018. Diagnosis Code Set General Equivalence Mappings. (2018). https:\/\/www.cms.gov\/medicare\/coding\/icd10\/2018-icd-10-cm-and-gems"},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Machine Learning. PMLR, 1568--1577","author":"Franceschi Luca","year":"2018","unstructured":"Luca Franceschi , Paolo Frasconi , Saverio Salzo , Riccardo Grazzi , and Massimiliano Pontil . 2018 . Bilevel programming for hyperparameter optimization and metalearning . In International Conference on Machine Learning. PMLR, 1568--1577 . Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimiliano Pontil. 2018. Bilevel programming for hyperparameter optimization and metalearning. In International Conference on Machine Learning. PMLR, 1568--1577."},{"key":"e_1_3_2_1_17_1","volume-title":"Hongjun Lu, and Philip S Yu.","author":"Cheong Fung Gabriel Pui","year":"2005","unstructured":"Gabriel Pui Cheong Fung , Jeffrey Xu Yu , Hongjun Lu, and Philip S Yu. 2005 . Text classification without negative examples revisit. IEEE transactions on Knowledge and Data Engineering 18, 1 (2005), 6--20. Gabriel Pui Cheong Fung, Jeffrey Xu Yu, Hongjun Lu, and Philip S Yu. 2005. Text classification without negative examples revisit. IEEE transactions on Knowledge and Data Engineering 18, 1 (2005), 6--20."},{"key":"e_1_3_2_1_18_1","volume-title":"Long short-term memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber . 1997. Long short-term memory. Neural computation 9, 8 ( 1997 ), 1735--1780. Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780."},{"key":"e_1_3_2_1_19_1","volume-title":"Proceedings of the 3rd International Conference for Learning Representations.","author":"Kingma Diederik P","year":"2015","unstructured":"Diederik P Kingma and Jimmy Ba . 2015 . Adam: A method for stochastic optimization . In Proceedings of the 3rd International Conference for Learning Representations. Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference for Learning Representations."},{"key":"e_1_3_2_1_20_1","volume-title":"Marthinus C Du Plessis, and Masashi Sugiyama","author":"Kiryo Ryuichi","year":"2017","unstructured":"Ryuichi Kiryo , Gang Niu , Marthinus C Du Plessis, and Masashi Sugiyama . 2017 . Positive-unlabeled learning with non-negative risk estimator. Advances in neural information processing systems 30 (2017). Ryuichi Kiryo, Gang Niu, Marthinus C Du Plessis, and Masashi Sugiyama. 2017. Positive-unlabeled learning with non-negative risk estimator. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_21_1","first-page":"448","article-title":"Learning with positive and unlabeled examples using weighted logistic regression","volume":"3","author":"Lee Wee Sun","year":"2003","unstructured":"Wee Sun Lee and Bing Liu . 2003 . Learning with positive and unlabeled examples using weighted logistic regression . In ICML , Vol. 3. 448 -- 455 . Wee Sun Lee and Bing Liu. 2003. Learning with positive and unlabeled examples using weighted logistic regression. In ICML, Vol. 3. 448--455.","journal-title":"ICML"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Fuyi Li Shuangyu Dong Andr\u00e9 Leier Meiya Han Xudong Guo Jing Xu Xiaoyu Wang Shirui Pan Cangzhi Jia Yang Zhang etal 2022. Positive-unlabeled learning in bioinformatics and computational biology: a brief review. Briefings in bioinformatics 23 1 (2022) bbab461. Fuyi Li Shuangyu Dong Andr\u00e9 Leier Meiya Han Xudong Guo Jing Xu Xiaoyu Wang Shirui Pan Cangzhi Jia Yang Zhang et al. 2022. Positive-unlabeled learning in bioinformatics and computational biology: a brief review. Briefings in bioinformatics 23 1 (2022) bbab461.","DOI":"10.1093\/bib\/bbab461"},{"key":"e_1_3_2_1_23_1","volume-title":"2022 IEEE 10th International Conference on Healthcare Informatics (ICHI). IEEE, 120--127","author":"Li Rui","year":"2022","unstructured":"Rui Li and Jing Gao . 2022 . Multi-modal contrastive learning for healthcare data analytics . In 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI). IEEE, 120--127 . Rui Li and Jing Gao. 2022. Multi-modal contrastive learning for healthcare data analytics. In 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI). IEEE, 120--127."},{"key":"e_1_3_2_1_24_1","volume-title":"AMIA Annual Symposium Proceedings","volume":"2021","author":"Li Rui","year":"2021","unstructured":"Rui Li , Fenglong Ma , and Jing Gao . 2021 . Integrating multimodal electronic health records for diagnosis prediction . In AMIA Annual Symposium Proceedings , Vol. 2021 . American Medical Informatics Association, 726. Rui Li, Fenglong Ma, and Jing Gao. 2021. Integrating multimodal electronic health records for diagnosis prediction. In AMIA Annual Symposium Proceedings, Vol. 2021. American Medical Informatics Association, 726."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"e_1_3_2_1_26_1","volume-title":"ICML","volume":"2","author":"Liu Bing","year":"2002","unstructured":"Bing Liu , Wee Sun Lee , Philip S Yu , and Xiaoli Li . 2002 . Partially supervised classification of text documents . In ICML , Vol. 2 . Sydney, NSW, 387--394. Bing Liu, Wee Sun Lee, Philip S Yu, and Xiaoli Li. 2002. Partially supervised classification of text documents. In ICML, Vol. 2. Sydney, NSW, 387--394."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403107"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098088"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976236.58"},{"key":"e_1_3_2_1_30_1","unstructured":"rarediseaseday.org. 2020. Long diagnosis misdiagnosis or no diagnosis - how rare diseases go under. (2020). https:\/\/www.rarediseaseday.org\/what-is-a-rare-disease\/ rarediseaseday.org. 2020. Long diagnosis misdiagnosis or no diagnosis - how rare diseases go under. (2020). https:\/\/www.rarediseaseday.org\/what-is-a-rare-disease\/"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-017-5678-9"},{"key":"e_1_3_2_1_32_1","volume-title":"Meta-weight-net: Learning an explicit mapping for sample weighting. Advances in neural information processing systems 32","author":"Shu Jun","year":"2019","unstructured":"Jun Shu , Qi Xie , Lixuan Yi , Qian Zhao , Sanping Zhou , Zongben Xu , and Deyu Meng . 2019 . Meta-weight-net: Learning an explicit mapping for sample weighting. Advances in neural information processing systems 32 (2019). Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng. 2019. Meta-weight-net: Learning an explicit mapping for sample weighting. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Guangxin Su Weitong Chen and Miao Xu. 2021. Positive-Unlabeled Learning from Imbalanced Data.. In IJCAI. 2995--3001. Guangxin Su Weitong Chen and Miao Xu. 2021. Positive-Unlabeled Learning from Imbalanced Data.. In IJCAI. 2995--3001.","DOI":"10.24963\/ijcai.2021\/412"},{"key":"e_1_3_2_1_34_1","volume-title":"Diffusion models: A comprehensive survey of methods and applications. arXiv preprint arXiv:2209.00796","author":"Yang Ling","year":"2022","unstructured":"Ling Yang , Zhilong Zhang , Yang Song , Shenda Hong , Runsheng Xu , Yue Zhao , Yingxia Shao , Wentao Zhang , Bin Cui , and Ming-Hsuan Yang . 2022. Diffusion models: A comprehensive survey of methods and applications. arXiv preprint arXiv:2209.00796 ( 2022 ). Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang. 2022. Diffusion models: A comprehensive survey of methods and applications. arXiv preprint arXiv:2209.00796 (2022)."},{"key":"e_1_3_2_1_35_1","volume-title":"Proceedings of International Conference on Machine Learning workshop.","author":"Yu Kezi","year":"2019","unstructured":"Kezi Yu , Yunlong Wang , Yong Cai , Cao Xiao , Emily Zhao , Lucas Glass , and Jimeng Sun . 2019 . Rare disease detection by sequence modeling with generative adversarial networks . In Proceedings of International Conference on Machine Learning workshop. Kezi Yu, Yunlong Wang, Yong Cai, Cao Xiao, Emily Zhao, Lucas Glass, and Jimeng Sun. 2019. Rare disease detection by sequence modeling with generative adversarial networks. In Proceedings of International Conference on Machine Learning workshop."},{"key":"e_1_3_2_1_36_1","volume-title":"Learning with local and global consistency. Advances in neural information processing systems 16","author":"Zhou Dengyong","year":"2003","unstructured":"Dengyong Zhou , Olivier Bousquet , Thomas Lal , Jason Weston , and Bernhard Sch\u00f6lkopf . 2003. Learning with local and global consistency. Advances in neural information processing systems 16 ( 2003 ). Dengyong Zhou, Olivier Bousquet, Thomas Lal, Jason Weston, and Bernhard Sch\u00f6lkopf. 2003. Learning with local and global consistency. Advances in neural information processing systems 16 (2003)."}],"event":{"name":"BCB '23: 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","sponsor":["SIGBio ACM Special Interest Group on Bioinformatics"],"location":"Houston TX USA","acronym":"BCB '23"},"container-title":["Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3584371.3612967","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3584371.3612967","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:46:25Z","timestamp":1750178785000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3584371.3612967"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,3]]},"references-count":36,"alternative-id":["10.1145\/3584371.3612967","10.1145\/3584371"],"URL":"https:\/\/doi.org\/10.1145\/3584371.3612967","relation":{},"subject":[],"published":{"date-parts":[[2023,9,3]]},"assertion":[{"value":"2023-10-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}