{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T06:31:17Z","timestamp":1768458677276,"version":"3.49.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T00:00:00Z","timestamp":1655078400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T00:00:00Z","timestamp":1655078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"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>Despite remarkable advances in cancer research, cancer remains one of the leading causes of death worldwide. Early detection of cancer and localization of the tissue of its origin are key to effective treatment. Here, we leverage technological advances in machine learning or artificial intelligence to design a novel framework for cancer diagnostics. Our proposed framework detects cancers and their tissues of origin using a unified model of cancers encompassing 33 cancers represented in The Cancer Genome Atlas (TCGA). Our model exploits the learned features of different cancers reflected in the respective dysregulated epigenomes, which arise early in carcinogenesis and differ remarkably between different cancer types or subtypes, thus holding a great promise in early cancer detection.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Our comprehensive assessment of the proposed model on the 33 different tissues of origin demonstrates its ability to detect and classify cancers to a high accuracy (&gt;\u200999% overall F-measure). Furthermore, our model distinguishes cancers from pre-cancerous lesions to metastatic tumors and discriminates between hypomethylation changes due to age related epigenetic drift and true cancer.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Beyond detection of primary cancers, our proposed computational model also robustly detects tissues of origin of secondary cancers, including metastatic cancers, second primary cancers, and cancers of unknown primaries. Our assessment revealed the ability of this model to characterize pre-cancer samples, a significant step forward in early cancer detection. Deployed broadly this model can deliver accurate diagnosis for a greatly expanded target patient population<jats:bold>.<\/jats:bold><\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-022-04783-y","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T19:30:37Z","timestamp":1655148637000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["CancerNet: a unified deep learning network for pan-cancer diagnostics"],"prefix":"10.1186","volume":"23","author":[{"given":"Steven","family":"Gore","sequence":"first","affiliation":[]},{"given":"Rajeev K.","family":"Azad","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,13]]},"reference":[{"key":"4783_CR1","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1186\/s13059-015-0699-9","volume":"16","author":"Z Yang","year":"2015","unstructured":"Yang Z, Jones A, Widschwendter M, Teschendorff AE. An integrative pan-cancer-wide analysis of epigenetic enzymes reveals universal patterns of epigenomic deregulation in cancer. Genome Biol. 2015;16:140.","journal-title":"Genome Biol"},{"key":"4783_CR2","doi-asserted-by":"publisher","first-page":"3248","DOI":"10.1186\/gb-2014-15-4-r54","volume":"15","author":"K Lokk","year":"2014","unstructured":"Lokk K, Modhukur V, Rajashekar B, M\u00e4rtens K, M\u00e4gi R, Kolde R, et al. DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns. Genome Biol. 2014;15:3248.","journal-title":"Genome Biol"},{"key":"4783_CR3","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1101\/gr.233213.117","volume":"28","author":"LA Salas","year":"2018","unstructured":"Salas LA, Wiencke JK, Koestler DC, Zhang Z, Christensen BC, Kelsey KT. Tracing human stem cell lineage during development using DNA methylation. Genome Res. 2018;28:1285\u201395.","journal-title":"Genome Res"},{"key":"4783_CR4","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1186\/s13148-015-0165-2","volume":"7","author":"N Sahnane","year":"2015","unstructured":"Sahnane N, Magnoli F, Bernasconi B, Tibiletti MG, Romualdi C, Pedroni M, et al. Aberrant DNA methylation profiles of inherited and sporadic colorectal cancer. Clin Epigenetics. 2015;7:131.","journal-title":"Clin Epigenetics"},{"key":"4783_CR5","doi-asserted-by":"publisher","first-page":"245","DOI":"10.2217\/epi.10.2","volume":"2","author":"JP Ross","year":"2010","unstructured":"Ross JP, Rand KN, Molloy PL. Hypomethylation of repeated DNA sequences in cancer. Epigenomics. 2010;2:245\u201369.","journal-title":"Epigenomics"},{"key":"4783_CR6","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1093\/nar\/gkv1038","volume":"44","author":"S Lee","year":"2016","unstructured":"Lee S, Wiemels JL. Genome-wide CpG island methylation and intergenic demethylation propensities vary among different tumor sites. Nucleic Acids Res. 2016;44:1105\u201317.","journal-title":"Nucleic Acids Res"},{"key":"4783_CR7","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.ygyno.2010.09.019","volume":"120","author":"TE Liggett","year":"2011","unstructured":"Liggett TE, Melnikov A, Yi Q, Replogle C, Hu W, Rotmensch J, et al. Distinctive DNA methylation patterns of cell-free plasma DNA in women with malignant ovarian tumors. Gynecol Oncol. 2011;120:113\u201320.","journal-title":"Gynecol Oncol"},{"key":"4783_CR8","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1016\/j.molonc.2014.10.012","volume":"9","author":"OA Stefansson","year":"2015","unstructured":"Stefansson OA, Moran S, Gomez A, Sayols S, Arribas-Jorba C, Sandoval J, et al. A DNA methylation-based definition of biologically distinct breast cancer subtypes. Mol Oncol. 2015;9:555\u201368.","journal-title":"Mol Oncol"},{"key":"4783_CR9","doi-asserted-by":"publisher","first-page":"3407","DOI":"10.1016\/j.celrep.2018.05.045","volume":"23","author":"F Bormann","year":"2018","unstructured":"Bormann F, Rodr\u00edguez-Paredes M, Lasitschka F, Edelmann D, Musch T, Benner A, et al. Cell-of-origin DNA methylation signatures are maintained during colorectal carcinogenesis. Cell Rep. 2018;23:3407\u201318.","journal-title":"Cell Rep"},{"key":"4783_CR10","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1038\/nature26000","volume":"555","author":"D Capper","year":"2018","unstructured":"Capper D, Jones DT, Sill M, Hovestadt V, Schrimpf D, Sturm D, et al. DNA methylation-based classification of central nervous system tumours. Nature. 2018;555:469\u201374.","journal-title":"Nature"},{"key":"4783_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13059-016-1129-3","volume":"18","author":"K Mundbjerg","year":"2017","unstructured":"Mundbjerg K, Chopra S, Alemozaffar M, Duymich C, Lakshminarasimhan R, Nichols PW, et al. Identifying aggressive prostate cancer foci using a DNA methylation classifier. Genome Biol. 2017;18:1\u201315.","journal-title":"Genome Biol"},{"key":"4783_CR12","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1097\/JTO.0000000000000560","volume":"10","author":"AI Robles","year":"2015","unstructured":"Robles AI, Arai E, Math\u00e9 EA, Okayama H, Schetter AJ, Brown D, et al. An integrated prognostic classifier for stage I lung adenocarcinoma based on mRNA, microRNA, and DNA methylation biomarkers. J Thorac Oncol. 2015;10:1037\u201348.","journal-title":"J Thorac Oncol"},{"key":"4783_CR13","doi-asserted-by":"publisher","first-page":"1425","DOI":"10.1002\/ijc.28790","volume":"135","author":"AR Brentnall","year":"2014","unstructured":"Brentnall AR, Vasiljevi\u0107 N, Scibior-Bentkowska D, Cadman L, Austin J, Szarewski A, et al. A DNA methylation classifier of cervical precancer based on human papillomavirus and human genes. Int J Cancer. 2014;135:1425\u201332.","journal-title":"Int J Cancer"},{"key":"4783_CR14","doi-asserted-by":"publisher","first-page":"93","DOI":"10.2353\/jmoldx.2008.070077","volume":"10","author":"AA Melnikov","year":"2008","unstructured":"Melnikov AA, Scholtens DM, Wiley EL, Khan SA, Levenson VV. Array-based multiplex analysis of DNA methylation in breast cancer tissues. J Mol Diagn. 2008;10:93\u2013101.","journal-title":"J Mol Diagn"},{"key":"4783_CR15","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1093\/bioinformatics\/btx622","volume":"34","author":"W Tang","year":"2018","unstructured":"Tang W, Wan S, Yang Z, Teschendorff AE, Zou Q. Tumor origin detection with tissue-specific miRNA and DNA methylation markers. Bioinformatics. 2018;34:398\u2013406.","journal-title":"Bioinformatics"},{"key":"4783_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13059-017-1191-5","volume":"18","author":"S Kang","year":"2017","unstructured":"Kang S, Li Q, Chen Q, Zhou Y, Park S, Lee G, et al. CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA. Genome Biol. 2017;18:1\u201312.","journal-title":"Genome Biol"},{"issue":"291\u2013304","key":"4783_CR17","volume":"173","author":"KA Hoadley","year":"2018","unstructured":"Hoadley KA, Yau C, Hinoue T, Wolf DM, Lazar AJ, Drill E, et al. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell. 2018;173(291\u2013304): e6.","journal-title":"Cell"},{"key":"4783_CR18","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0226461","volume":"15","author":"C Zheng","year":"2020","unstructured":"Zheng C, Xu R. Predicting cancer origins with a DNA methylation-based deep neural network model. PLoS ONE. 2020;15: e0226461.","journal-title":"PLoS ONE"},{"key":"4783_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/ncomms9699","volume":"6","author":"J Wei","year":"2015","unstructured":"Wei J, Haddad A, Wu K, Zhao H, Kapur P, Zhang Z, et al. A CpG-methylation-based assay to predict survival in clear cell renal cell carcinoma. Nat Commun. 2015;6:1\u201311.","journal-title":"Nat Commun"},{"key":"4783_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12935-020-01345-1","volume":"20","author":"Z Tian","year":"2020","unstructured":"Tian Z, Meng L, Long X, Diao T, Hu M, Wang M, et al. DNA methylation-based classification and identification of bladder cancer prognosis-associated subgroups. Cancer Cell Int. 2020;20:1\u201311.","journal-title":"Cancer Cell Int"},{"key":"4783_CR21","first-page":"1","volume":"1","author":"SP Wu","year":"2017","unstructured":"Wu SP, Cooper BT, Bu F, Bowman CJ, Killian JK, Serrano J, et al. DNA methylation-based classifier for accurate molecular diagnosis of bone sarcomas. JCO Precis Oncol. 2017;1:1\u201311.","journal-title":"JCO Precis Oncol"},{"key":"4783_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12935-019-0900-4","volume":"19","author":"W Chen","year":"2019","unstructured":"Chen W, Zhuang J, Wang PP, Jiang J, Lin C, Zeng P, et al. DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups. Cancer Cell Int. 2019;19:1\u201314.","journal-title":"Cancer Cell Int"},{"key":"4783_CR23","first-page":"174474","volume":"12","author":"GP Way","year":"2017","unstructured":"Way GP, Greene CS. Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders. BioRxiv. 2017;12:174474.","journal-title":"BioRxiv"},{"key":"4783_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41592-019-0576-7","volume":"16","author":"M Amodio","year":"2019","unstructured":"Amodio M, Van Dijk D, Srinivasan K, Chen WS, Mohsen H, Moon KR, et al. Exploring single-cell data with deep multitasking neural networks. Nat Methods. 2019;16:1\u20137.","journal-title":"Nat Methods"},{"issue":"380\u2013394","key":"4783_CR25","volume":"8","author":"JN Taroni","year":"2019","unstructured":"Taroni JN, Grayson PC, Hu Q, Eddy S, Kretzler M, Merkel PA, et al. MultiPLIER: a transfer learning framework for transcriptomics reveals systemic features of rare disease. Cell Syst. 2019;8(380\u2013394): e4.","journal-title":"Cell Syst"},{"key":"4783_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-019-3130-9","volume":"20","author":"Z Wang","year":"2019","unstructured":"Wang Z, Wang Y. Extracting a biologically latent space of lung cancer epigenetics with variational autoencoders. BMC Bioinform. 2019;20:1\u20137.","journal-title":"BMC Bioinform"},{"key":"4783_CR27","doi-asserted-by":"publisher","first-page":"e201900517","DOI":"10.26508\/lsa.201900517","volume":"2","author":"J Ronen","year":"2019","unstructured":"Ronen J, Hayat S, Akalin A. Evaluation of colorectal cancer subtypes and cell lines using deep learning. Life Sci Alliance. 2019;2:e201900517.","journal-title":"Life Sci Alliance"},{"key":"4783_CR28","unstructured":"Kingma DP, Welling M. Auto-encoding variational bayes. 2013. arXiv:1312.6114."},{"key":"4783_CR29","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S et al. Generative adversarial nets. In: Advances in neural information processing systems. 2014."},{"key":"4783_CR30","unstructured":"Chollet, Fran ccois and others. Keras. 2015.\nhttps:\/\/keras.io."},{"key":"4783_CR31","unstructured":"Abadi M et al. TensorFlow: large-scale machine learning on heterogeneous systems. 2015."},{"key":"4783_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2382577.2382579","volume":"6","author":"S Kaufman","year":"2012","unstructured":"Kaufman S, Rosset S, Perlich C, Stitelman O. Leakage in data mining: formulation, detection, and avoidance. ACM Trans Knowl Discov Data. 2012;6:1\u201321.","journal-title":"ACM Trans Knowl Discov Data"},{"key":"4783_CR33","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/j.ccr.2014.07.014","volume":"26","author":"CF Davis","year":"2014","unstructured":"Davis CF, Ricketts CJ, Wang M, Yang L, Cherniack AD, Shen H, et al. The somatic genomic landscape of chromophobe renal cell carcinoma. Cancer Cell. 2014;26:319\u201330.","journal-title":"Cancer Cell"},{"key":"4783_CR34","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1186\/1471-2407-10-227","volume":"10","author":"PW Ang","year":"2010","unstructured":"Ang PW, Loh M, Liem N, Lim PL, Grieu F, Vaithilingam A, et al. Comprehensive profiling of DNA methylation in colorectal cancer reveals subgroups with distinct clinicopathological and molecular features. BMC Cancer. 2010;10:227.","journal-title":"BMC Cancer"},{"issue":"194\u2013212","key":"4783_CR35","volume":"23","author":"JD Campbell","year":"2018","unstructured":"Campbell JD, Yau C, Bowlby R, Liu Y, Brennan K, Fan H, et al. Genomic, pathway network, and immunologic features distinguishing squamous carcinomas. Cell Rep. 2018;23(194\u2013212): e6.","journal-title":"Cell Rep"},{"key":"4783_CR36","doi-asserted-by":"publisher","first-page":"5574","DOI":"10.1002\/cam4.2474","volume":"8","author":"H Dillek\u00e5s","year":"2019","unstructured":"Dillek\u00e5s H, Rogers MS, Straume O. Are 90% of deaths from cancer caused by metastases? Cancer Med. 2019;8:5574\u20136.","journal-title":"Cancer Med"},{"key":"4783_CR37","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1007\/s11864-013-0257-1","volume":"14","author":"FA Greco","year":"2013","unstructured":"Greco FA. Molecular diagnosis of the tissue of origin in cancer of unknown primary site: useful in patient management. Curr Treat Options Oncol. 2013;14:634\u201342.","journal-title":"Curr Treat Options Oncol"},{"key":"4783_CR38","doi-asserted-by":"publisher","first-page":"1990","DOI":"10.1016\/S0959-8049(03)00547-1","volume":"39","author":"N Pavlidis","year":"2003","unstructured":"Pavlidis N, Briasoulis E, Hainsworth J, Greco FA. Diagnostic and therapeutic management of cancer of an unknown primary. Eur J Cancer. 2003;39:1990\u20132005.","journal-title":"Eur J Cancer"},{"key":"4783_CR39","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.maturitas.2009.02.005","volume":"63","author":"JV Lacey Jr","year":"2009","unstructured":"Lacey JV Jr, Chia VM. Endometrial hyperplasia and the risk of progression to carcinoma. Maturitas. 2009;63:39\u201344.","journal-title":"Maturitas"},{"key":"4783_CR40","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1038\/nrclinonc.2017.97","volume":"14","author":"S Moran","year":"2017","unstructured":"Moran S, Martinez-Card\u00fas A, Boussios S, Esteller M. Precision medicine based on epigenomics: the paradigm of carcinoma of unknown primary. Nat Rev Clin Oncol. 2017;14:682.","journal-title":"Nat Rev Clin Oncol"},{"key":"4783_CR41","doi-asserted-by":"publisher","first-page":"5400","DOI":"10.1038\/sj.onc.1205651","volume":"21","author":"M Ehrlich","year":"2002","unstructured":"Ehrlich M. DNA methylation in cancer: too much, but also too little. Oncogene. 2002;21:5400\u201313.","journal-title":"Oncogene"},{"key":"4783_CR42","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1093\/ajcp\/99.6.729","volume":"99","author":"K Sheahan","year":"1993","unstructured":"Sheahan K, O\u2019Keane JC, Abramowitz A, Carlson JA, Burke B, Gottlieb LS, et al. Metastatic adenocarcinoma of an unknown primary site: a comparison of the relative contributions of morphology, minimal essential clinical data and CEA immunostaining status. Am J Clin Pathol. 1993;99:729\u201335.","journal-title":"Am J Clin Pathol"},{"key":"4783_CR43","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1186\/s13148-018-0496-x","volume":"10","author":"AG van der Heijden","year":"2018","unstructured":"van der Heijden AG, Mengual L, Ingelmo-Torres M, Lozano JJ, Baixauli M, Geavlete B, et al. Urine cell-based DNA methylation classifier for monitoring bladder cancer. Clin Epigenetics. 2018;10:71.","journal-title":"Clin Epigenetics"},{"key":"4783_CR44","doi-asserted-by":"publisher","first-page":"3603","DOI":"10.1158\/1055-9965.EPI-08-0507","volume":"17","author":"CT Viet","year":"2008","unstructured":"Viet CT, Schmidt BL. Methylation array analysis of preoperative and postoperative saliva DNA in oral cancer patients. Cancer Epidemiol Prev Biomark. 2008;17:3603\u201311.","journal-title":"Cancer Epidemiol Prev Biomark"},{"key":"4783_CR45","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1373\/clinchem.2014.222679","volume":"61","author":"E Heitzer","year":"2015","unstructured":"Heitzer E, Ulz P, Geigl JB. Circulating tumor DNA as a liquid biopsy for cancer. Clin Chem. 2015;61:112\u201323.","journal-title":"Clin Chem"},{"key":"4783_CR46","doi-asserted-by":"publisher","first-page":"E5503","DOI":"10.1073\/pnas.1508736112","volume":"112","author":"K Sun","year":"2015","unstructured":"Sun K, Jiang P, Chan KA, Wong J, Cheng YK, Liang RH, et al. Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments. Proc Natl Acad Sci. 2015;112:E5503\u201312.","journal-title":"Proc Natl Acad Sci"},{"key":"4783_CR47","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1038\/nm.1789","volume":"14","author":"F Diehl","year":"2008","unstructured":"Diehl F, Schmidt K, Choti MA, Romans K, Goodman S, Li M, et al. Circulating mutant DNA to assess tumor dynamics. Nat Med. 2008;14:985\u201390.","journal-title":"Nat Med"},{"key":"4783_CR48","doi-asserted-by":"publisher","first-page":"22424","DOI":"10.1126\/scitranslmed.3007094","volume":"6","author":"C Bettegowda","year":"2014","unstructured":"Bettegowda C, Sausen M, Leary RJ, Kinde I, Wang Y, Agrawal N, et al. Detection of circulating tumor DNA in early-and late-stage human malignancies. Sci Transl Med. 2014;6:22424.","journal-title":"Sci Transl Med"},{"key":"4783_CR49","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0008274","volume":"4","author":"AE Teschendorff","year":"2009","unstructured":"Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Gayther SA, Apostolidou S, et al. An epigenetic signature in peripheral blood predicts active ovarian cancer. PLoS ONE. 2009;4: e8274.","journal-title":"PLoS ONE"},{"key":"4783_CR50","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1038\/s41586-018-0703-0","volume":"563","author":"SY Shen","year":"2018","unstructured":"Shen SY, Singhania R, Fehringer G, Chakravarthy A, Roehrl MH, Chadwick D, et al. Sensitive tumour detection and classification using plasma cell-free DNA methylomes. Nature. 2018;563:579\u201383.","journal-title":"Nature"},{"key":"4783_CR51","doi-asserted-by":"publisher","first-page":"18761","DOI":"10.1073\/pnas.1313995110","volume":"110","author":"KA Chan","year":"2013","unstructured":"Chan KA, Jiang P, Chan CW, Sun K, Wong J, Hui EP, et al. Noninvasive detection of cancer-associated genome-wide hypomethylation and copy number aberrations by plasma DNA bisulfite sequencing. Proc Natl Acad Sci. 2013;110:18761\u20138.","journal-title":"Proc Natl Acad Sci"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04783-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-022-04783-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04783-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T00:03:49Z","timestamp":1668384229000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-022-04783-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,13]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["4783"],"URL":"https:\/\/doi.org\/10.1186\/s12859-022-04783-y","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,13]]},"assertion":[{"value":"3 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 June 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2022","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. Please note that we have used only publicly available cancer data from The Cancer Genome Atlas (TCGA) (). Our work presented here does not involve any human subjects and this is not considered human subjects research. The relevant guidelines and regulations (Helsinki declarations\/national\/institutional guidelines) are therefore not applicable here.","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 declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"229"}}