{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T06:34:03Z","timestamp":1781073243619,"version":"3.54.1"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"Supplement_2","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Startup Grant, ShanghaiTech University","award":["ECCB2022"],"award-info":[{"award-number":["ECCB2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,16]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Detecting synthetic lethality (SL) is a promising strategy for identifying anti-cancer drug targets. Targeting SL partners of a primary gene mutated in cancer is selectively lethal to cancer cells. Due to high cost of wet-lab experiments and availability of gold standard SL data, supervised machine learning for SL prediction has been popular. However, most of the methods are based on binary classification and thus limited by the lack of reliable negative data. Contrastive learning can train models without any negative sample and is thus promising for finding novel SLs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We propose NSF4SL, a negative-sample-free SL prediction model based on a contrastive learning framework. It captures the characteristics of positive SL samples by using two branches of neural networks that interact with each other to learn SL-related gene representations. Moreover, a feature-wise data augmentation strategy is used to mitigate the sparsity of SL data. NSF4SL significantly outperforms all baselines which require negative samples, even in challenging experimental settings. To the best of our knowledge, this is the first time that SL prediction is formulated as a gene ranking problem, which is more practical than the current formulation as binary classification. NSF4SL is the first contrastive learning method for SL prediction and its success points to a new direction of machine-learning methods for identifying novel SLs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>Our source code is available at https:\/\/github.com\/JieZheng-ShanghaiTech\/NSF4SL.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac462","type":"journal-article","created":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T09:13:59Z","timestamp":1663665239000},"page":"ii13-ii19","source":"Crossref","is-referenced-by-count":18,"title":["NSF4SL: negative-sample-free contrastive learning for ranking synthetic lethal partner genes in human cancers"],"prefix":"10.1093","volume":"38","author":[{"given":"Shike","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, ShanghaiTech University , Shanghai 201210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yimiao","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, ShanghaiTech University , Shanghai 201210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, ShanghaiTech University , Shanghai 201210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[{"name":"Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University , Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR) , Singapore 138632, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6774-9786","authenticated-orcid":false,"given":"Jie","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, ShanghaiTech University , Shanghai 201210, China"},{"name":"Shanghai Engineering Research Center of Intelligent Vision and Imaging , Shanghai 201210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,9,18]]},"reference":[{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"e1006888","DOI":"10.1371\/journal.pcbi.1006888","article-title":"Predicting synthetic lethal interactions using conserved patterns in protein interaction networks","volume":"15","author":"Benstead-Hume","year":"2019","journal-title":"PLoS Comput. Biol"},{"key":"2023041408003611200_","first-page":"2787","article-title":"Translating embeddings for modeling multi-relational data","volume":"26","author":"Bordes","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"W317","DOI":"10.1093\/nar\/gkab447","article-title":"KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis","volume":"49","author":"Bu","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"4458","DOI":"10.1093\/bioinformatics\/btaa211","article-title":"Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers","volume":"36","author":"Cai","year":"2020","journal-title":"Bioinformatics"},{"key":"2023041408003611200_","first-page":"15750","author":"Chen","year":"2021"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-021-04210-8","article-title":"Contrastive self-supervised clustering of scRNA-seq data","volume":"22","author":"Ciortan","year":"2021","journal-title":"BMC Bioinformatics"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"160","DOI":"10.3389\/fcell.2020.00160","article-title":"Fanconi anemia pathway: mechanisms of breast cancer predisposition development and potential therapeutic targets","volume":"8","author":"Fang","year":"2020","journal-title":"Front. Cell Dev. Biol"},{"key":"2023041408003611200_","first-page":"21271","article-title":"Bootstrap your own latent\u2014a new approach to self-supervised learning","volume":"33","author":"Grill","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"D1011","DOI":"10.1093\/nar\/gkv1108","article-title":"SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets","volume":"44","author":"Guo","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023041408003611200_","author":"Han","year":"2021"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"4041","DOI":"10.1109\/JBHI.2021.3079302","article-title":"Prediction of synthetic lethal interactions in human cancers using multi-view graph auto-encoder","volume":"25","author":"Hao","year":"2021","journal-title":"IEEE J. Biomed. Health Inform"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-019-3197-3","article-title":"Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization","volume":"20","author":"Huang","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1056\/NEJMe0903044","article-title":"Synthetic lethality\u2014a new direction in cancer-drug development","volume":"361","author":"Iglehart","year":"2009","journal-title":"N. Engl. J. Med"},{"key":"2023041408003611200_","first-page":"448","author":"Ioffe","year":"2015"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"e1004506","DOI":"10.1371\/journal.pcbi.1004506","article-title":"Connectivity homology enables inter-species network models of synthetic lethality","volume":"11","author":"Jacunski","year":"2015","journal-title":"PLoS Comput. Biol"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1145\/582415.582418","article-title":"Cumulated gain-based evaluation of IR techniques","volume":"20","author":"J\u00e4rvelin","year":"2002","journal-title":"ACM Trans. Inf. Syst"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1016\/j.cell.2014.07.027","article-title":"Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality","volume":"158","author":"Jerby-Arnon","year":"2014","journal-title":"Cell"},{"key":"2023041408003611200_","first-page":"317","author":"Lee","year":"2021"},{"key":"2023041408003611200_","first-page":"1","article-title":"Harnessing synthetic lethality to predict the response to cancer treatment","volume":"9","author":"Lee","year":"2018","journal-title":"Nat. Commun"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1109\/TCBB.2019.2909908","article-title":"SL2MF: predicting synthetic lethality in human cancers via logistic matrix factorization","volume":"17","author":"Liu","year":"2020","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"2432","DOI":"10.1093\/bioinformatics\/btab110","article-title":"Graph contextualized attention network for predicting synthetic lethality in human cancers","volume":"37","author":"Long","year":"2021","journal-title":"Bioinformatics"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1126\/science.aam7344","article-title":"PARP inhibitors: the first synthetic lethal targeted therapy","volume":"355","author":"Lord","year":"2017","journal-title":"Science"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.febslet.2010.11.024","article-title":"Synthetic lethality: general principles, utility and detection using genetic screens in human cells","volume":"585","author":"Nijman","year":"2011","journal-title":"FEBS Lett"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.canlet.2020.02.016","article-title":"Synthetic lethality: a promising therapeutic strategy for hepatocellular carcinoma","volume":"476","author":"Tang","year":"2020","journal-title":"Cancer Lett"},{"key":"2023041408003611200_","author":"Thakoor","year":"2021"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1111\/pcmr.12573","article-title":"Synthetic lethality: emerging targets and opportunities in melanoma","volume":"30","author":"Thompson","year":"2017","journal-title":"Pigment Cell Melanoma Res"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1016\/j.cell.2017.06.010","article-title":"Defining a cancer dependency map","volume":"170","author":"Tsherniak","year":"2017","journal-title":"Cell"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1016\/j.jmb.2017.04.018","article-title":"Exploiting synthetic lethality and network biology to overcome EGFR inhibitor resistance in lung cancer","volume":"429","author":"Vyse","year":"2017","journal-title":"J. Mol. Biol"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"112","DOI":"10.3389\/fphar.2020.00112","article-title":"EXP2SL: a machine learning framework for cell-line-specific synthetic lethality prediction","volume":"11","author":"Wan","year":"2020","journal-title":"Front. Pharmacol"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1038\/s41575-021-00465-x","article-title":"Exploring liver cancer biology through functional genetic screens","volume":"18","author":"Wang","year":"2021","journal-title":"Nat. Rev. Gastroenterol. Hepatol"},{"key":"2023041408003611200_","article-title":"Computational methods, databases and tools for synthetic lethality prediction","volume":"23","author":"Wang","year":"2022","journal-title":"Brief. Bioinform"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","DOI":"10.1093\/database\/baac030","article-title":"SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery","volume":"2022","author":"Wang","year":"2022","journal-title":"Database"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"i418","DOI":"10.1093\/bioinformatics\/btab271","article-title":"KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers","volume":"37","author":"Wang","year":"2021","journal-title":"Bioinformatics"},{"key":"2023041408003611200_","author":"Xu","year":"2015"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"9038","DOI":"10.7150\/thno.63416","article-title":"Mapping the landscape of synthetic lethal interactions in liver cancer","volume":"11","author":"Yang","year":"2021","journal-title":"Theranostics"},{"key":"2023041408003611200_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-019-1745-9","article-title":"GEMINI: a variational Bayesian approach to identify genetic interactions from combinatorial CRISPR screens","volume":"20","author":"Zamanighomi","year":"2019","journal-title":"Genome Biol"},{"key":"2023041408003611200_","first-page":"1","article-title":"The tumor therapy landscape of synthetic lethality","volume":"12","author":"Zhang","year":"2021","journal-title":"Nat. Commun"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/Supplement_2\/ii13\/49886444\/btac462.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/Supplement_2\/ii13\/49886444\/btac462.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T04:07:31Z","timestamp":1700971651000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/38\/Supplement_2\/ii13\/6701996"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,1]]},"references-count":37,"journal-issue":{"issue":"Supplement_2","published-print":{"date-parts":[[2022,9,16]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btac462","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,9,1]]},"published":{"date-parts":[[2022,9,1]]}}}