{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T17:18:17Z","timestamp":1792603097487,"version":"4.5.0"},"reference-count":89,"publisher":"American Association for the Advancement of Science (AAAS)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T00:00:00Z","timestamp":1647907200000},"content-version":"vor","delay-in-days":80,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001407","name":"Department of Biotechnology, Ministry of Science and Technology, India","doi-asserted-by":"publisher","award":["BT\/PR40151\/BTIS\/137\/5\/2021"],"award-info":[{"award-number":["BT\/PR40151\/BTIS\/137\/5\/2021"]}],"id":[{"id":"10.13039\/501100001407","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001412","name":"Council of Scientific and Industrial Research, India","doi-asserted-by":"publisher","award":["09\/512(0212)\/2016-EMR-I"],"award-info":[{"award-number":["09\/512(0212)\/2016-EMR-I"]}],"id":[{"id":"10.13039\/501100001412","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001412","name":"Council of Scientific and Industrial Research, India","doi-asserted-by":"publisher","award":["09\/512(0227)\/2017-EMR-I"],"award-info":[{"award-number":["09\/512(0227)\/2017-EMR-I"]}],"id":[{"id":"10.13039\/501100001412","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004330","name":"GlaxoSmithKline","doi-asserted-by":"publisher","award":["RCB\/PhD-BI\/2020\/1016"],"award-info":[{"award-number":["RCB\/PhD-BI\/2020\/1016"]}],"id":[{"id":"10.13039\/100004330","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["csbj.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Comput Struct Biotechnol J"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1016\/j.csbj.2022.03.019","type":"journal-article","created":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T21:37:15Z","timestamp":1648071435000},"page":"1618-1631","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":74,"title":["Machine learning assisted analysis of breast cancer gene expression profiles reveals novel potential prognostic biomarkers for triple-negative breast cancer"],"prefix":"10.34133","volume":"20","author":[{"given":"Anamika","family":"Thalor","sequence":"first","affiliation":[{"name":"Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hemant","family":"Kumar Joon","sequence":"additional","affiliation":[{"name":"Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India"},{"name":"Regional Centre for Biotechnology, Faridabad 121001, Haryana, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gagandeep","family":"Singh","sequence":"additional","affiliation":[{"name":"Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shikha","family":"Roy","sequence":"additional","affiliation":[{"name":"Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dinesh","family":"Gupta","sequence":"additional","affiliation":[{"name":"Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2022,4,7]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"crossref","unstructured":"Sung H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021. 71(3): p. 209-249.","DOI":"10.3322\/caac.21660"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpet.2018.02.004"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1089\/ars.2015.6546"},{"key":"e_1_3_4_5_2","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.amsu.2019.11.021","article-title":"Molecular classification of breast cancer: A retrospective cohort study","volume":"49","author":"Al-Thoubaity F.K.","year":"2020","unstructured":"Al-Thoubaity F.K.: Molecular classification of breast cancer: A retrospective cohort study. Ann Med Surg (Lond) 2020; 49: 44.","journal-title":"Ann Med Surg (Lond)"},{"key":"e_1_3_4_6_2","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.1093\/annonc\/mdr304","article-title":"Strategies for subtypes\u2013dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011","volume":"22","author":"Goldhirsch A.","year":"2011","unstructured":"Goldhirsch A. et al: Strategies for subtypes\u2013dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol 2011; 22: 1736.","journal-title":"Ann Oncol"},{"key":"e_1_3_4_7_2","doi-asserted-by":"crossref","first-page":"2206","DOI":"10.1093\/annonc\/mdt303","article-title":"Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013","volume":"24","author":"Goldhirsch A.","year":"2013","unstructured":"Goldhirsch A. et al: Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol 2013; 24: 2206.","journal-title":"Ann Oncol"},{"key":"e_1_3_4_8_2","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1158\/1078-0432.CCR-07-1658","article-title":"Basal-like breast cancer defined by five biomarkers has superior prognostic value than triple-negative phenotype","volume":"14","author":"Cheang M.C.","year":"2008","unstructured":"Cheang M.C. et al: Basal-like breast cancer defined by five biomarkers has superior prognostic value than triple-negative phenotype. Clin Cancer Res 2008; 14: 1368.","journal-title":"Clin Cancer Res"},{"key":"e_1_3_4_9_2","first-page":"106","article-title":"Current approaches in treatment of triple-negative breast cancer","volume":"12","author":"Wahba H.A.","year":"2015","unstructured":"Wahba H.A. and El-Hadaad H.A.: Current approaches in treatment of triple-negative breast cancer. Cancer Biol Med 2015; 12: 106.","journal-title":"Cancer Biol Med"},{"key":"e_1_3_4_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10549-016-4059-6"},{"key":"e_1_3_4_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(16)32454-0"},{"key":"e_1_3_4_12_2","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1038\/bjc.2014.444","article-title":"Predicting response and survival in chemotherapy-treated triple-negative breast cancer","volume":"111","author":"Prat A.","year":"2014","unstructured":"Prat A. et al: Predicting response and survival in chemotherapy-treated triple-negative breast cancer. Br J Cancer 2014; 111: 1532.","journal-title":"Br J Cancer"},{"key":"e_1_3_4_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13346-018-0551-3"},{"key":"e_1_3_4_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2011.09.024"},{"key":"e_1_3_4_15_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrm3758"},{"key":"e_1_3_4_16_2","doi-asserted-by":"publisher","DOI":"10.1080\/19336918.2015.1016686"},{"key":"e_1_3_4_17_2","doi-asserted-by":"publisher","DOI":"10.1634\/theoncologist.2017-0398"},{"key":"e_1_3_4_18_2","doi-asserted-by":"crossref","first-page":"5463","DOI":"10.1002\/cncr.27581","article-title":"Clinicopathologic features, patterns of recurrence, and survival among women with triple-negative breast cancer in the National Comprehensive Cancer Network","volume":"118","author":"Lin N.U.","year":"2012","unstructured":"Lin N.U. et al: Clinicopathologic features, patterns of recurrence, and survival among women with triple-negative breast cancer in the National Comprehensive Cancer Network. Cancer 2012; 118: 5463.","journal-title":"Cancer"},{"key":"e_1_3_4_19_2","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1007\/s10549-010-1293-1","article-title":"Triple negative breast cancer: unmet medical needs","volume":"125","author":"Pal S.K.","year":"2011","unstructured":"Pal S.K., Childs B.H. and Pegram M.: Triple negative breast cancer: unmet medical needs. Breast Cancer Res Treat 2011; 125: 627.","journal-title":"Breast Cancer Res Treat"},{"key":"e_1_3_4_20_2","article-title":"Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine","author":"Chen Z.H.","year":"2021","unstructured":"Chen Z.H. et al: Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun (Lond) 2021; .","journal-title":"Cancer Commun (Lond)"},{"key":"e_1_3_4_21_2","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1186\/s12915-021-01002-7","article-title":"A cell-to-patient machine learning transfer approach uncovers novel basal-like breast cancer prognostic markers amongst alternative splice variants","volume":"19","author":"Villemin J.P.","year":"2021","unstructured":"Villemin J.P. et al: A cell-to-patient machine learning transfer approach uncovers novel basal-like breast cancer prognostic markers amongst alternative splice variants. BMC Biol 2021; 19: 70.","journal-title":"BMC Biol"},{"key":"e_1_3_4_22_2","doi-asserted-by":"crossref","first-page":"10464","DOI":"10.1038\/s41598-020-67525-1","article-title":"Machine learning analysis identifies genes differentiating triple negative breast cancers","volume":"10","author":"Kothari C.","year":"2020","unstructured":"Kothari C. et al: Machine learning analysis identifies genes differentiating triple negative breast cancers. Sci Rep 2020; 10: 10464.","journal-title":"Sci Rep"},{"key":"e_1_3_4_23_2","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxp059"},{"key":"e_1_3_4_24_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btg405"},{"key":"e_1_3_4_25_2","doi-asserted-by":"publisher","DOI":"10.1038\/nprot.2009.97"},{"key":"e_1_3_4_26_2","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1186\/1471-2105-12-322","article-title":"Strategies for aggregating gene expression data: the collapseRows R function","volume":"12","author":"Miller J.A.","year":"2011","unstructured":"Miller J.A. et al: Strategies for aggregating gene expression data: the collapseRows R function. BMC Bioinf 2011; 12: 322.","journal-title":"BMC Bioinf"},{"key":"e_1_3_4_27_2","unstructured":"B B. preprocessCore: A collection of pre-processing functions. R package version 1.54.0. https:\/\/github.com\/bmbolstad\/preprocessCore.; 2021."},{"key":"e_1_3_4_28_2","unstructured":"Leek JT J.W. Parker HS Fertig EJ Jaffe AE Zhang Y Storey JD Torres LC sva: Surrogate Variable Analysis. R package version 3.40.0. 2021."},{"key":"e_1_3_4_29_2","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1186\/s12859-018-2263-6","article-title":"Alternative empirical Bayes models for adjusting for batch effects in genomic studies","volume":"19","author":"Zhang Y.","year":"2018","unstructured":"Zhang Y. et al: Alternative empirical Bayes models for adjusting for batch effects in genomic studies. BMC Bioinf 2018; 19: 262.","journal-title":"BMC Bioinf"},{"key":"e_1_3_4_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.11.077"},{"key":"e_1_3_4_31_2","first-page":"9","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Chen T.G.C.","year":"2016","unstructured":"Chen T.G.C.: XGBoost: A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. : 2016: 9."},{"key":"e_1_3_4_32_2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"Breiman L.","year":"2001","unstructured":"Breiman L.: Machine Learning 2001; 45: 5.","journal-title":"Machine Learning"},{"key":"e_1_3_4_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00058655"},{"key":"e_1_3_4_34_2","doi-asserted-by":"crossref","unstructured":"Boser B.E.G. I.M.; Vapnik V.N. A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on Computational learning theory \u2013 COLT . CiteSeerX; 1992.","DOI":"10.1145\/130385.130401"},{"key":"e_1_3_4_35_2","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression","volume":"46","author":"Altman N.S.","year":"1992","unstructured":"Altman N.S.: An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression. Am Statistic 1992; 46: 175.","journal-title":"Am Statistic"},{"key":"e_1_3_4_36_2","unstructured":"Breiman L.F. J.H. Olshen R.A. Stone C.J. Classification and regression trees. Monterey CA: Wadsworth & Brooks\/Cole Advanced Books & Software; 1984."},{"key":"e_1_3_4_37_2","first-page":"4","article-title":"A Study on Support Vector Machine based Linear and Non-Linear Pattern Classification","author":"Ghosh Sourish D.A.","year":"2019","unstructured":"Ghosh Sourish D.A. and Aleena S.: A Study on Support Vector Machine based Linear and Non-Linear Pattern Classification. IEEE 2019; : 4.","journal-title":"IEEE"},{"key":"e_1_3_4_38_2","doi-asserted-by":"crossref","first-page":"28808","DOI":"10.1109\/ACCESS.2019.2955754","article-title":"Medical Health Big Data Classification Based on KNN Classification Algorithm","volume":"8","author":"Xing W.","year":"2020","unstructured":"Xing W. and Bei Y.: Medical Health Big Data Classification Based on KNN Classification Algorithm. IEEE Access 2020; 8: 28808.","journal-title":"IEEE Access"},{"key":"e_1_3_4_39_2","volume-title":"Decision Tree","author":"F\u00fcrnkranz J.","year":"2010","unstructured":"F\u00fcrnkranz J.: Decision Tree. Boston, MA: Encyclopedia of Machine Learning. Springer 2010."},{"key":"e_1_3_4_40_2","doi-asserted-by":"crossref","unstructured":"John G Tate S.B. Harry C Jubb Zbyslaw Sondka David M Beare Nidhi Bindal Harry Boutselakis Charlotte G Cole Celestino Creatore Elisabeth Dawson Peter Fish Bhavana Harsha Charlie Hathaway Steve C Jupe Chai Yin Kok Kate Noble Laura Ponting Christopher C Ramshaw Claire E Rye Helen E Speedy Ray Stefancsik Sam L Thompson Shicai Wang Sari Ward Peter J Campbell Simon A Forbes COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Res; 2019. 47: p. 7.","DOI":"10.1093\/nar\/gky1015"},{"key":"e_1_3_4_41_2","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.2642"},{"key":"e_1_3_4_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.02.060"},{"key":"e_1_3_4_43_2","unstructured":"Gentleman R C.V. Huber W Hahne F genefilter: genefilter: methods for filtering genes from high-throughput experiments. R package version 1.74.0. 2021."},{"key":"e_1_3_4_44_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkv007"},{"key":"e_1_3_4_45_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10549-009-0674-9"},{"key":"e_1_3_4_46_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkw937"},{"key":"e_1_3_4_47_2","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/1471-2105-4-2","article-title":"An automated method for finding molecular complexes in large protein interaction networks","volume":"4","author":"Bader G.D.","year":"2003","unstructured":"Bader G.D. and Hogue C.W.: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinf 2003; 4: 2.","journal-title":"BMC Bioinf"},{"key":"e_1_3_4_48_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkaa970"},{"key":"e_1_3_4_49_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkn923"},{"key":"e_1_3_4_50_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/28.1.27"},{"key":"e_1_3_4_51_2","doi-asserted-by":"crossref","first-page":"1734","DOI":"10.1038\/s41467-017-01430-6","article-title":"An extracellular matrix-related prognostic and predictive indicator for early-stage non-small cell lung cancer","volume":"8","author":"Lim S.B.","year":"2017","unstructured":"Lim S.B. et al: An extracellular matrix-related prognostic and predictive indicator for early-stage non-small cell lung cancer. Nat Commun 2017; 8: 1734.","journal-title":"Nat Commun"},{"key":"e_1_3_4_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2021.07.014"},{"key":"e_1_3_4_53_2","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1007\/s12282-019-00988-x","article-title":"Identification of differentially expressed genes between triple and non-triple-negative breast cancer using bioinformatics analysis","volume":"26","author":"Zhai Q.","year":"2019","unstructured":"Zhai Q. et al: Identification of differentially expressed genes between triple and non-triple-negative breast cancer using bioinformatics analysis. Breast Cancer 2019; 26: 784.","journal-title":"Breast Cancer"},{"key":"e_1_3_4_54_2","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1159\/000494162","article-title":"Comprehensive Analysis of Differentially Expressed Profiles of lncRNAs\/mRNAs and miRNAs with Associated ceRNA Networks in Triple-Negative Breast Cancer","volume":"50","author":"Yang R.","year":"2018","unstructured":"Yang R. et al: Comprehensive Analysis of Differentially Expressed Profiles of lncRNAs\/mRNAs and miRNAs with Associated ceRNA Networks in Triple-Negative Breast Cancer. Cell Physiol Biochem 2018; 50: 473.","journal-title":"Cell Physiol Biochem"},{"key":"e_1_3_4_55_2","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1186\/s12885-021-08647-1","article-title":"Using a machine learning approach to identify key prognostic molecules for esophageal squamous cell carcinoma","volume":"21","author":"Li M.X.","year":"2021","unstructured":"Li M.X. et al: Using a machine learning approach to identify key prognostic molecules for esophageal squamous cell carcinoma. BMC Cancer 2021; 21: 906.","journal-title":"BMC Cancer"},{"key":"e_1_3_4_56_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbadis.2020.165822"},{"key":"e_1_3_4_57_2","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1038\/s41598-020-58290-2","article-title":"Machine Learning and Network Analyses Reveal Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics","volume":"10","author":"Sinkala M.","year":"2020","unstructured":"Sinkala M., Mulder N. and Martin D.: Machine Learning and Network Analyses Reveal Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics. Sci Rep 2020; 10: 1212.","journal-title":"Sci Rep"},{"key":"e_1_3_4_58_2","doi-asserted-by":"publisher","DOI":"10.1158\/2159-8290.CD-20-0288"},{"key":"e_1_3_4_59_2","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1186\/s13058-019-1118-z","article-title":"Prognostic and predictive value of androgen receptor expression in postmenopausal women with estrogen receptor-positive breast cancer: results from the Breast International Group Trial 1\u201398","volume":"21","author":"Kensler K.H.","year":"2019","unstructured":"Kensler K.H. et al: Prognostic and predictive value of androgen receptor expression in postmenopausal women with estrogen receptor-positive breast cancer: results from the Breast International Group Trial 1\u201398. Breast Cancer Res 2019; 21: 30.","journal-title":"Breast Cancer Res"},{"key":"e_1_3_4_60_2","first-page":"937","article-title":"ZIC1 acts a tumor suppressor in breast cancer by targeting survivin","volume":"53","author":"Han W.","year":"2018","unstructured":"Han W. et al: ZIC1 acts a tumor suppressor in breast cancer by targeting survivin. Int J Oncol 2018; 53: 937.","journal-title":"Int J Oncol"},{"key":"e_1_3_4_61_2","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1159\/000430464","article-title":"Calpain in Breast Cancer: Role in Disease Progression and Treatment Response","volume":"82","author":"Storr S.J.","year":"2015","unstructured":"Storr S.J. et al: Calpain in Breast Cancer: Role in Disease Progression and Treatment Response. Pathobiology 2015; 82: 133.","journal-title":"Pathobiology"},{"key":"e_1_3_4_62_2","doi-asserted-by":"publisher","DOI":"10.1038\/onc.2008.101"},{"key":"e_1_3_4_63_2","first-page":"557","article-title":"Identification of key genes as potential biomarkers for triplenegative breast cancer using integrating genomics analysis","volume":"21","author":"Zhong G.","year":"2020","unstructured":"Zhong G. et al: Identification of key genes as potential biomarkers for triplenegative breast cancer using integrating genomics analysis. Mol Med Rep 2020; 21: 557.","journal-title":"Mol Med Rep"},{"key":"e_1_3_4_64_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2021.102451"},{"key":"e_1_3_4_65_2","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms6987"},{"key":"e_1_3_4_66_2","doi-asserted-by":"crossref","first-page":"2689","DOI":"10.1158\/1535-7163.MCT-18-0399","article-title":"ST8SIA1 Regulates Tumor Growth and Metastasis in TNBC by Activating the FAK-AKT-mTOR Signaling Pathway","volume":"17","author":"Nguyen K.","year":"2018","unstructured":"Nguyen K. et al: ST8SIA1 Regulates Tumor Growth and Metastasis in TNBC by Activating the FAK-AKT-mTOR Signaling Pathway. Mol Cancer Ther 2018; 17: 2689.","journal-title":"Mol Cancer Ther"},{"key":"e_1_3_4_67_2","doi-asserted-by":"publisher","DOI":"10.1084\/jem.20121846"},{"key":"e_1_3_4_68_2","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1186\/1476-4598-12-61","article-title":"BCL11A overexpression predicts survival and relapse in non-small cell lung cancer and is modulated by microRNA-30a and gene amplification","volume":"12","author":"Jiang B.Y.","year":"2013","unstructured":"Jiang B.Y. et al: BCL11A overexpression predicts survival and relapse in non-small cell lung cancer and is modulated by microRNA-30a and gene amplification. Mol Cancer 2013; 12: 61.","journal-title":"Mol Cancer"},{"key":"e_1_3_4_69_2","doi-asserted-by":"publisher","DOI":"10.1074\/jbc.RA120.014018"},{"key":"e_1_3_4_70_2","doi-asserted-by":"publisher","DOI":"10.1038\/ni925"},{"key":"e_1_3_4_71_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1213394110"},{"key":"e_1_3_4_72_2","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1158\/1078-0432.CCR-09-1029","article-title":"New insights into checkpoint kinase 1 in the DNA damage response signaling network","volume":"16","author":"Dai Y.","year":"2010","unstructured":"Dai Y. and Grant S.: New insights into checkpoint kinase 1 in the DNA damage response signaling network. Clin Cancer Res 2010; 16: 376.","journal-title":"Clin Cancer Res"},{"key":"e_1_3_4_73_2","doi-asserted-by":"crossref","first-page":"12172","DOI":"10.1038\/s41598-021-91290-4","article-title":"Multiomics analysis reveals CT83 is the most specific gene for triple negative breast cancer and its hypomethylation is oncogenic in breast cancer","volume":"11","author":"Chen C.","year":"2021","unstructured":"Chen C. et al: Multiomics analysis reveals CT83 is the most specific gene for triple negative breast cancer and its hypomethylation is oncogenic in breast cancer. Sci Rep 2021; 11: 12172.","journal-title":"Sci Rep"},{"key":"e_1_3_4_74_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.2174\/157339506775471901","article-title":"BOB.1\/OBF.1 - A Critical Regulator of B Cell Function","volume":"2","author":"Brunner C.","year":"2006","unstructured":"Brunner C. and Wirth T.: BOB.1\/OBF.1 - A Critical Regulator of B Cell Function. Curr Immunol Rev 2006; 2: 3.","journal-title":"Curr Immunol Rev"},{"key":"e_1_3_4_75_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.it.2003.08.002"},{"key":"e_1_3_4_76_2","doi-asserted-by":"publisher","DOI":"10.1101\/sqb.1999.64.119"},{"key":"e_1_3_4_77_2","doi-asserted-by":"publisher","DOI":"10.1038\/383542a0"},{"key":"e_1_3_4_78_2","doi-asserted-by":"crossref","first-page":"3214","DOI":"10.1002\/eji.1830261255","article-title":"B lymphocytes are impaired in mice lacking the transcriptional co-activator Bob1\/OCA-B\/OBF1","volume":"26","author":"Nielsen P.J.","year":"1996","unstructured":"Nielsen P.J. et al: B lymphocytes are impaired in mice lacking the transcriptional co-activator Bob1\/OCA-B\/OBF1. Eur J Immunol 1996; 26: 3214.","journal-title":"Eur J Immunol"},{"key":"e_1_3_4_79_2","doi-asserted-by":"publisher","DOI":"10.1038\/383538a0"},{"key":"e_1_3_4_80_2","doi-asserted-by":"crossref","first-page":"3159","DOI":"10.4049\/jimmunol.1502400","article-title":"POU2AF1 Functions in the Human Airway Epithelium To Regulate Expression of Host Defense Genes","volume":"196","author":"Zhou H.","year":"2016","unstructured":"Zhou H. et al: POU2AF1 Functions in the Human Airway Epithelium To Regulate Expression of Host Defense Genes. J Immunol 2016; 196: 3159.","journal-title":"J Immunol"},{"key":"e_1_3_4_81_2","doi-asserted-by":"publisher","DOI":"10.1093\/dnares\/dsp013"},{"key":"e_1_3_4_82_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.0105-2896.2005.00281.x"},{"key":"e_1_3_4_83_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.semcancer.2017.08.002"},{"key":"e_1_3_4_84_2","first-page":"433","article-title":"S100B expression in breast cancer as a predictive marker for cancer metastasis","volume":"52","author":"Yen M.C.","year":"2018","unstructured":"Yen M.C. et al: S100B expression in breast cancer as a predictive marker for cancer metastasis. Int J Oncol 2018; 52: 433.","journal-title":"Int J Oncol"},{"key":"e_1_3_4_85_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bcp.2008.07.023"},{"key":"e_1_3_4_86_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0002-9440(10)65204-2"},{"key":"e_1_3_4_87_2","doi-asserted-by":"crossref","first-page":"4416","DOI":"10.1038\/ncomms5416","article-title":"Increased Notch signalling inhibits anoikis and stimulates proliferation of prostate luminal epithelial cells","volume":"5","author":"Kwon O.J.","year":"2014","unstructured":"Kwon O.J. et al: Increased Notch signalling inhibits anoikis and stimulates proliferation of prostate luminal epithelial cells. Nat Commun 2014; 5: 4416.","journal-title":"Nat Commun"},{"key":"e_1_3_4_88_2","doi-asserted-by":"crossref","first-page":"2561","DOI":"10.2741\/3872","article-title":"Role of Rho GTPases and their regulators in cancer progression","volume":"16","author":"Rathinam R.","year":"2011","unstructured":"Rathinam R., Berrier A. and Alahari S.K.: Role of Rho GTPases and their regulators in cancer progression. Front Biosci (Landmark Ed) 2011; 16: 2561.","journal-title":"Front Biosci (Landmark Ed)"},{"key":"e_1_3_4_89_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2006.10.018"},{"key":"e_1_3_4_90_2","doi-asserted-by":"crossref","first-page":"2645","DOI":"10.1158\/1535-7163.MCT-09-0383","article-title":"NF-kappaB activation in endothelial cells is critical for the activity of angiostatic agents","volume":"8","author":"Tabruyn S.P.","year":"2009","unstructured":"Tabruyn S.P. et al: NF-kappaB activation in endothelial cells is critical for the activity of angiostatic agents. Mol Cancer Ther 2009; 8: 2645.","journal-title":"Mol Cancer Ther"}],"container-title":["Computational and Structural Biotechnology Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2001037022000940?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2001037022000940?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/spj.science.org\/doi\/pdf\/10.1016\/j.csbj.2022.03.019","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T12:58:53Z","timestamp":1779022733000},"score":1,"resource":{"primary":{"URL":"https:\/\/spj.science.org\/doi\/10.1016\/j.csbj.2022.03.019"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":89,"alternative-id":["10.1016\/j.csbj.2022.03.019"],"URL":"https:\/\/doi.org\/10.1016\/j.csbj.2022.03.019","relation":{},"ISSN":["2001-0370"],"issn-type":[{"value":"2001-0370","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Machine learning assisted analysis of breast cancer gene expression profiles reveals novel potential prognostic biomarkers for triple-negative breast cancer","name":"articletitle","label":"Article Title"},{"value":"Computational and Structural Biotechnology Journal","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.csbj.2022.03.019","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.","name":"copyright","label":"Copyright"}]}}