{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T06:27:42Z","timestamp":1791959262733,"version":"4.3.4"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"S4","license":[{"start":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T00:00:00Z","timestamp":1635120000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T00:00:00Z","timestamp":1635120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1R01LM013337"],"award-info":[{"award-number":["1R01LM013337"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100001831","name":"Lynn Sage Cancer Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001831","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Genetic information is becoming more readily available and is increasingly being used to predict patient cancer types as well as their subtypes. Most classification methods thus far utilize somatic mutations as independent features for classification and are limited by study power. We aim to develop a novel method to effectively explore the landscape of genetic variants, including germline variants, and small insertions and deletions for cancer type prediction.\n<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We proposed DeepCues, a deep learning model that utilizes convolutional neural networks to unbiasedly derive features from raw cancer DNA sequencing data for disease classification and relevant gene discovery. Using raw whole-exome sequencing as features, germline variants and somatic mutations, including insertions and deletions, were interactively amalgamated for feature generation and cancer prediction. We applied DeepCues to a dataset from TCGA to classify seven different types of major cancers and obtained an overall accuracy of 77.6%. We compared DeepCues to conventional methods and demonstrated a significant overall improvement (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001). Strikingly, using DeepCues, the top 20 breast cancer relevant genes we have identified, had a 40% overlap with the top 20 known breast cancer driver genes.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Our results support DeepCues as a novel method to improve the representational resolution of DNA sequencings and its power in deriving features from raw sequences for cancer type prediction, as well as discovering new cancer relevant genes.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-021-04400-4","type":"journal-article","created":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T02:02:34Z","timestamp":1635127354000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Deep learning for cancer type classification and driver gene identification"],"prefix":"10.1186","volume":"22","author":[{"given":"Zexian","family":"Zeng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengsheng","family":"Mao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andy","family":"Vo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janna Ore","family":"Nugent","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seema A.","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan E.","family":"Clare","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0195-7456","authenticated-orcid":false,"given":"Yuan","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,10,25]]},"reference":[{"issue":"3","key":"4400_CR1","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1038\/nrc1299","volume":"4","author":"PA Futreal","year":"2004","unstructured":"Futreal PA, Coin L, Marshall M, Down T, Hubbard T, Wooster R, Rahman N, Stratton MR. A census of human cancer genes. Nat Rev Cancer. 2004;4(3):177.","journal-title":"Nat Rev Cancer"},{"issue":"2","key":"4400_CR2","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.cell.2018.02.060","volume":"173","author":"MH Bailey","year":"2018","unstructured":"Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A, Colaprico A, Wendl MC, Kim J, Reardon B. Comprehensive characterization of cancer driver genes and mutations. Cell. 2018;173(2):371-385.e318.","journal-title":"Cell"},{"issue":"7239","key":"4400_CR3","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1038\/nature07943","volume":"458","author":"MR Stratton","year":"2009","unstructured":"Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature. 2009;458(7239):719.","journal-title":"Nature"},{"issue":"6788","key":"4400_CR4","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1038\/35015718","volume":"405","author":"NJ Risch","year":"2000","unstructured":"Risch NJ. Searching for genetic determinants in the new millennium. Nature. 2000;405(6788):847\u201356.","journal-title":"Nature"},{"issue":"5","key":"4400_CR5","doi-asserted-by":"publisher","first-page":"e1003054","DOI":"10.1371\/journal.pcbi.1003054","volume":"9","author":"MD Leiserson","year":"2013","unstructured":"Leiserson MD, Blokh D, Sharan R, Raphael BJ. Simultaneous identification of multiple driver pathways in cancer. PLoS Comput Biol. 2013;9(5):e1003054.","journal-title":"PLoS Comput Biol"},{"issue":"3","key":"4400_CR6","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1093\/jmcb\/mjv026","volume":"7","author":"RD Melamed","year":"2015","unstructured":"Melamed RD, Wang J, Iavarone A, Rabadan R. An information theoretic method to identify combinations of genomic alterations that promote glioblastoma. J Mol Cell Biol. 2015;7(3):203\u201313.","journal-title":"J Mol Cell Biol"},{"issue":"Suppl.1","key":"4400_CR7","first-page":"69","volume":"13","author":"Y Luo","year":"2014","unstructured":"Luo Y, Riedlinger G, Szolovits P. Text mining in cancer gene and pathway prioritization. Cancer Inform. 2014;13(Suppl.1):69.","journal-title":"Cancer Inform"},{"issue":"1","key":"4400_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41523-020-0165-z","volume":"6","author":"Z Zeng","year":"2020","unstructured":"Zeng Z, Vo A, Li X, Shidfar A, Saldana P, Blanco L, Xuei X, Luo Y, Khan SA, Clare SE. Somatic genetic aberrations in benign breast disease and the risk of subsequent breast cancer. NPJ Breast Cancer. 2020;6(1):1\u201311.","journal-title":"NPJ Breast Cancer"},{"issue":"34","key":"4400_CR9","doi-asserted-by":"publisher","first-page":"56780","DOI":"10.18632\/oncotarget.18187","volume":"8","author":"J Cai","year":"2017","unstructured":"Cai J, Ye Q, Luo S, Zhuang Z, He K, Zhuo Z-J, Wan X, Cheng J. CASP8-652 6N insertion\/deletion polymorphism and overall cancer risk: evidence from 49 studies. Oncotarget. 2017;8(34):56780.","journal-title":"Oncotarget"},{"issue":"3","key":"4400_CR10","doi-asserted-by":"publisher","first-page":"274","DOI":"10.5301\/jbm.5000253","volume":"32","author":"C Li","year":"2017","unstructured":"Li C, Feng L, Niu L, Li TT, Zhang B, Wan H, Zhu Z, Liu H, Wang K, Fu H. An insertion\/deletion polymorphism within the promoter of EGLN2 is associated with susceptibility to colorectal cancer. Int J Biol Markers. 2017;32(3):274\u20137.","journal-title":"Int J Biol Markers"},{"issue":"D1","key":"4400_CR11","doi-asserted-by":"publisher","first-page":"D848","DOI":"10.1093\/nar\/gkaa809","volume":"49","author":"Y Cui","year":"2021","unstructured":"Cui Y, Cheng X, Chen Q, Song B, Chiu A, Gao Y, Dawson T, Chao L, Zhang W, Li D. CRISP-view: a database of functional genetic screens spanning multiple phenotypes. Nucleic Acids Res. 2021;49(D1):D848\u201354.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"4400_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13059-020-02166-1","volume":"21","author":"SS Gu","year":"2020","unstructured":"Gu SS, Wang X, Hu X, Jiang P, Li Z, Traugh N, Bu X, Tang Q, Wang C, Zeng Z. Clonal tracing reveals diverse patterns of response to immune checkpoint blockade. Genome Biol. 2020;21(1):1\u201328.","journal-title":"Genome Biol"},{"key":"4400_CR13","unstructured":"Domingos P, Pazzani M: Beyond independence: conditions for the optimality of the simple Bayesian classier. In: Proc 13th intl conf machine learning; 1996. p. 105\u2013112."},{"issue":"3","key":"4400_CR14","doi-asserted-by":"publisher","first-page":"1287","DOI":"10.1214\/09-AOS691","volume":"38","author":"P Ravikumar","year":"2010","unstructured":"Ravikumar P, Wainwright MJ, Lafferty JD. High-dimensional Ising model selection using \u21131-regularized logistic regression. Ann Stat. 2010;38(3):1287\u2013319.","journal-title":"Ann Stat"},{"issue":"1","key":"4400_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41523-020-00192-7","volume":"6","author":"Z Zeng","year":"2020","unstructured":"Zeng Z, Amin A, Roy A, Pulliam NE, Karavites LC, Espino S, Helenowski I, Li X, Luo Y, Khan SA. Preoperative magnetic resonance imaging use and oncologic outcomes in premenopausal breast cancer patients. NPJ Breast Cancer. 2020;6(1):1\u20138.","journal-title":"NPJ Breast Cancer"},{"key":"4400_CR16","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.patrec.2017.09.036","volume":"109","author":"S Zhang","year":"2018","unstructured":"Zhang S, Cheng D, Deng Z, Zong M, Deng X. A novel kNN algorithm with data-driven k parameter computation. Pattern Recogn Lett. 2018;109:44\u201354.","journal-title":"Pattern Recogn Lett"},{"issue":"6","key":"4400_CR17","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1093\/bib\/bbx044","volume":"19","author":"R Miotto","year":"2018","unstructured":"Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2018;19(6):1236\u201346.","journal-title":"Brief Bioinform"},{"issue":"7","key":"4400_CR18","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1158\/0008-5472.CAN-17-3486","volume":"78","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Manjunath M, Zhang S, Chasman D, Roy S, Song JS. Integrative genomic analysis predicts causative cis-regulatory mechanisms of the breast cancer-associated genetic variant rs4415084. Can Res. 2018;78(7):1579\u201391.","journal-title":"Can Res"},{"key":"4400_CR19","doi-asserted-by":"publisher","first-page":"754","DOI":"10.3389\/fgene.2019.00754","volume":"10","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Manjunath M, Yan J, Baur BA, Zhang S, Roy S, Song JS. The cancer-associated genetic variant Rs3903072 modulates immune cells in the tumor microenvironment. Front Genet. 2019;10:754.","journal-title":"Front Genet"},{"issue":"7553","key":"4400_CR20","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436.","journal-title":"Nature"},{"issue":"5","key":"4400_CR21","doi-asserted-by":"publisher","first-page":"1445","DOI":"10.1021\/acs.molpharmaceut.5b00982","volume":"13","author":"P Mamoshina","year":"2016","unstructured":"Mamoshina P, Vieira A, Putin E, Zhavoronkov A. Applications of deep learning in biomedicine. Mol Pharm. 2016;13(5):1445\u201354.","journal-title":"Mol Pharm"},{"issue":"7","key":"4400_CR22","doi-asserted-by":"publisher","first-page":"878","DOI":"10.15252\/msb.20156651","volume":"12","author":"C Angermueller","year":"2016","unstructured":"Angermueller C, P\u00e4rnamaa T, Parts L, Stegle O. Deep learning for computational biology. Mol Syst Biol. 2016;12(7):878.","journal-title":"Mol Syst Biol"},{"key":"4400_CR23","doi-asserted-by":"crossref","unstructured":"Mao C, Yao L, Pan Y, Luo Y, Zeng Z: Deep generative classifiers for thoracic disease diagnosis with chest x-ray images. In: 2018. IEEE. p. 1209\u20131214.","DOI":"10.1109\/BIBM.2018.8621107"},{"issue":"11","key":"4400_CR24","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278\u2013324.","journal-title":"Proc IEEE"},{"issue":"8","key":"4400_CR25","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1038\/nbt.3300","volume":"33","author":"B Alipanahi","year":"2015","unstructured":"Alipanahi B, Delong A, Weirauch MT, Frey BJ. Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nat Biotechnol. 2015;33(8):831.","journal-title":"Nat Biotechnol"},{"issue":"11","key":"4400_CR26","doi-asserted-by":"publisher","first-page":"e107","DOI":"10.1093\/nar\/gkw226","volume":"44","author":"D Quang","year":"2016","unstructured":"Quang D, Xie X. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res. 2016;44(11):e107\u2013e107.","journal-title":"Nucleic Acids Res"},{"issue":"10","key":"4400_CR27","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1038\/nmeth.3547","volume":"12","author":"J Zhou","year":"2015","unstructured":"Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods. 2015;12(10):931.","journal-title":"Nat Methods"},{"issue":"1","key":"4400_CR28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-016-1139-1","volume":"18","author":"C Angermueller","year":"2017","unstructured":"Angermueller C, Lee HJ, Reik W, Stegle O. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol. 2017;18(1):1\u201313.","journal-title":"Genome Biol"},{"issue":"10","key":"4400_CR29","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1038\/ng.2764","volume":"45","author":"JN Weinstein","year":"2013","unstructured":"Weinstein JN, Collisson EA, Mills GB, Shaw KRM, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM, Network CGAR. The cancer genome atlas pan-cancer analysis project. Nat Genet. 2013;45(10):1113.","journal-title":"Nat Genet"},{"issue":"9","key":"4400_CR30","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1101\/gr.107524.110","volume":"20","author":"A McKenna","year":"2010","unstructured":"McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297\u2013303.","journal-title":"Genome Res"},{"issue":"2","key":"4400_CR31","doi-asserted-by":"publisher","first-page":"80","DOI":"10.4161\/fly.19695","volume":"6","author":"P Cingolani","year":"2012","unstructured":"Cingolani P, Platts A, le Wang L, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly. 2012;6(2):80\u201392.","journal-title":"Fly"},{"issue":"Database issue","key":"4400_CR32","doi-asserted-by":"publisher","first-page":"D945","DOI":"10.1093\/nar\/gkq929","volume":"39","author":"SA Forbes","year":"2011","unstructured":"Forbes SA, Bindal N, Bamford S, Cole C, Kok CY, Beare D, Jia M, Shepherd R, Leung K, Menzies A, et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 2011;39(Database issue):D945-950.","journal-title":"Nucleic Acids Res"},{"issue":"4154","key":"4400_CR33","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1126\/science.185.4154.862","volume":"185","author":"R Grantham","year":"1974","unstructured":"Grantham R. Amino acid difference formula to help explain protein evolution. Science. 1974;185(4154):862\u20134.","journal-title":"Science"},{"issue":"1","key":"4400_CR34","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1186\/s13073-017-0493-2","volume":"9","author":"KP Soh","year":"2017","unstructured":"Soh KP, Szczurek E, Sakoparnig T, Beerenwinkel N. Predicting cancer type from tumour DNA signatures. Genome Med. 2017;9(1):104.","journal-title":"Genome Med"},{"issue":"2","key":"4400_CR35","doi-asserted-by":"publisher","first-page":"e1000384","DOI":"10.1371\/journal.pgen.1000384","volume":"5","author":"BE Madsen","year":"2009","unstructured":"Madsen BE, Browning SR. A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet. 2009;5(2):e1000384.","journal-title":"PLoS Genet"},{"issue":"1","key":"4400_CR36","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.ajhg.2011.05.029","volume":"89","author":"MC Wu","year":"2011","unstructured":"Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet. 2011;89(1):82\u201393.","journal-title":"Am J Hum Genet"},{"issue":"5685","key":"4400_CR37","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1126\/science.1099870","volume":"305","author":"JC Cohen","year":"2004","unstructured":"Cohen JC, Kiss RS, Pertsemlidis A, Marcel YL, McPherson R, Hobbs HH. Multiple rare alleles contribute to low plasma levels of HDL cholesterol. Science (New York, NY). 2004;305(5685):869\u201372.","journal-title":"Science (New York, NY)"},{"issue":"45","key":"4400_CR38","doi-asserted-by":"publisher","first-page":"15992","DOI":"10.1073\/pnas.0407187101","volume":"101","author":"NS Fearnhead","year":"2004","unstructured":"Fearnhead NS, Wilding JL, Winney B, Tonks S, Bartlett S, Bicknell DC, Tomlinson IP, Mortensen NJM, Bodmer WF. Multiple rare variants in different genes account for multifactorial inherited susceptibility to colorectal adenomas. Proc Natl Acad Sci. 2004;101(45):15992\u20137.","journal-title":"Proc Natl Acad Sci"},{"key":"4400_CR39","doi-asserted-by":"crossref","unstructured":"Luo Y, Mao C: PANTHER: pathway augmented nonnegative tensor factorization for HighER-order feature learning. In: Proceedings of the AAAI conference on artificial intelligence; 2021.","DOI":"10.1609\/aaai.v35i1.16113"},{"key":"4400_CR40","doi-asserted-by":"publisher","first-page":"103247","DOI":"10.1016\/j.jbi.2019.103247","volume":"96","author":"Z Zeng","year":"2019","unstructured":"Zeng Z, Vo AH, Mao C, Clare SE, Khan SA, Luo Y. Cancer classification and pathway discovery using non-negative matrix factorization. J Biomed Inform. 2019;96:103247.","journal-title":"J Biomed Inform"},{"key":"4400_CR41","doi-asserted-by":"publisher","first-page":"e155","DOI":"10.7717\/peerj-cs.155","volume":"4","author":"M Manjunath","year":"2018","unstructured":"Manjunath M, Zhang Y, Yeo SH, Sobh O, Russell N, Followell C, Bushell C, Ravaioli U, Song JS. ClusterEnG: an interactive educational web resource for clustering and visualizing high-dimensional data. PeerJ Comput Sci. 2018;4:e155.","journal-title":"PeerJ Comput Sci"},{"issue":"8","key":"4400_CR42","first-page":"1438","volume":"35","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Manjunath M, Kim Y, Heintz J, Song JS. SequencEnG: an interactive knowledge base of sequencing techniques. Bioinformatics (Oxford, England). 2019;35(8):1438\u201340.","journal-title":"Bioinformatics (Oxford, England)"},{"key":"4400_CR43","unstructured":"Luo Y, Mao C: ScanMap: supervised confounding aware non-negative matrix factorization for polygenic risk modeling. In: Machine learning for healthcare conference: 2020. PMLR. p. 27\u201345."},{"issue":"11","key":"4400_CR44","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1038\/s41568-018-0060-1","volume":"18","author":"Z Sondka","year":"2018","unstructured":"Sondka Z, Bamford S, Cole CG, Ward SA, Dunham I, Forbes SA. The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers. Nat Rev Cancer. 2018;18(11):696\u2013705.","journal-title":"Nat Rev Cancer"},{"issue":"7132","key":"4400_CR45","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1038\/nature05610","volume":"446","author":"C Greenman","year":"2007","unstructured":"Greenman C, Stephens P, Smith R, Dalgliesh GL, Hunter C, Bignell G, Davies H, Teague J, Butler A, Stevens C. Patterns of somatic mutation in human cancer genomes. Nature. 2007;446(7132):153.","journal-title":"Nature"},{"issue":"5","key":"4400_CR46","doi-asserted-by":"publisher","first-page":"1308","DOI":"10.1086\/375121","volume":"72","author":"H Meijers-Heijboer","year":"2003","unstructured":"Meijers-Heijboer H, Wasielewski M, Wagner A, Hollestelle A, Elstrodt F, van den Bos R, de Snoo A, Fat GTA, Brekelmans C, Jagmohan S. The CHEK2 1100delC mutation identifies families with a hereditary breast and colorectal cancer phenotype. Am J Hum Genet. 2003;72(5):1308\u201314.","journal-title":"Am J Hum Genet"},{"issue":"4","key":"4400_CR47","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/5254.708428","volume":"13","author":"MA Hearst","year":"1998","unstructured":"Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B. Support vector machines. IEEE Intell Syst Appl. 1998;13(4):18\u201328.","journal-title":"IEEE Intell Syst Appl"},{"issue":"4","key":"4400_CR48","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1111\/j.1467-9868.2010.00740.x","volume":"72","author":"N Meinshausen","year":"2010","unstructured":"Meinshausen N, B\u00fchlmann P. Stability selection. J R Stat Soc Ser B (Stat Methodol). 2010;72(4):417\u201373.","journal-title":"J R Stat Soc Ser B (Stat Methodol)"},{"key":"4400_CR49","doi-asserted-by":"crossref","unstructured":"Zeng Z, Mao C, Vo A, Nugent JO, Khan SA, Clare SE, Luo Y. Deep learning for cancer type classification. bioRxiv 2019:612762.","DOI":"10.1101\/612762"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04400-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-021-04400-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04400-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T23:03:35Z","timestamp":1675811015000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-021-04400-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,25]]},"references-count":49,"journal-issue":{"issue":"S4","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["4400"],"URL":"https:\/\/doi.org\/10.1186\/s12859-021-04400-4","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,25]]},"assertion":[{"value":"12 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 October 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors have no competing interests to declare.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"491"}}