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In addition to other areas, they are finding their applications in cancer prognosis and diagnosis. However, in this area, the research community is lagging behind the technology. A systematic review along with a taxonomy on ensemble methods used in cancer prognosis and diagnosis can pave the way for the research community to keep pace with the technology and even lead trend. In this article, we first present an overview on existing relevant surveys and highlight their shortcomings, which raise the need for a new survey focusing on Ensemble Classifiers (ECs) used for the diagnosis and prognosis of different cancer types. Then, we exhaustively review the existing methods, including the traditional ones as well as those based on deep learning. The review leads to a taxonomy as well as the identification of the best-studied cancer types, the best ensemble methods used for the related purposes, the prevailing input data types, the most common decision-making strategies, and the common evaluating methodologies. Moreover, we establish future directions for researchers interested in following existing research trends or working on less-studied aspects of the area.<\/jats:p>","DOI":"10.1145\/3580218","type":"journal-article","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T11:54:06Z","timestamp":1673956446000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":32,"title":["Cancer Prognosis and Diagnosis Methods Based on Ensemble Learning"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6691-0988","authenticated-orcid":false,"given":"Behrouz","family":"Zolfaghari","sequence":"first","affiliation":[{"name":"Department of Mathematics, Faculty of Education and Integrated Arts and Sciences, Waseda University, Ey\u00fcpsultan, Istanbul, Turkey, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7083-6685","authenticated-orcid":false,"given":"Leila","family":"Mirsadeghi","sequence":"additional","affiliation":[{"name":"Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3301-0232","authenticated-orcid":false,"given":"Khodakhast","family":"Bibak","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1906-3912","authenticated-orcid":false,"given":"Kaveh","family":"Kavousi","sequence":"additional","affiliation":[{"name":"Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,3,3]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.ins.2021.07.024","article-title":"BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification","volume":"577","author":"Abdar Moloud","year":"2021","unstructured":"Moloud Abdar, Mohammad Amin Fahami, Satarupa Chakrabarti, Abbas Khosravi, Pawe\u0142 P\u0142awiak, U. Rajendra Acharya, Ryszard Tadeusiewicz, and Saeid Nahavandi. 2021. BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification. Inf. Sci. 577 (2021), 353\u2013378.","journal-title":"Inf. Sci."},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.measurement.2019.05.022","article-title":"CWV-BANN-SVM ensemble learning classifier for an accurate diagnosis of breast cancer","volume":"146","author":"Abdar Moloud","year":"2019","unstructured":"Moloud Abdar and Vladimir Makarenkov. 2019. CWV-BANN-SVM ensemble learning classifier for an accurate diagnosis of breast cancer. Measurement 146 (2019), 557\u2013570.","journal-title":"Measurement"},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","first-page":"104418","DOI":"10.1016\/j.compbiomed.2021.104418","article-title":"Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning","volume":"135","author":"Abdar Moloud","year":"2021","unstructured":"Moloud Abdar, Maryam Samami, Sajjad Dehghani Mahmoodabad, Thang Doan, Bogdan Mazoure, Reza Hashemifesharaki, Li Liu, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, et al. 2021. Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning. Comput. Biol. Med. 135 (2021), 104418.","journal-title":"Comput. Biol. Med."},{"key":"e_1_3_2_5_2","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.patrec.2018.11.004","article-title":"A new nested ensemble technique for automated diagnosis of breast cancer","volume":"132","author":"Abdar Moloud","year":"2020","unstructured":"Moloud Abdar, Mariam Zomorodi-Moghadam, Xujuan Zhou, Raj Gururajan, Xiaohui Tao, Prabal D. Barua, and Rashmi Gururajan. 2020. A new nested ensemble technique for automated diagnosis of breast cancer. Pattern Recog. Lett. 132 (2020), 123\u2013131.","journal-title":"Pattern Recog. Lett."},{"issue":"3","key":"e_1_3_2_6_2","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1093\/bioinformatics\/btp630","article-title":"Robust biomarker identification for cancer diagnosis with ensemble feature selection methods","volume":"26","author":"Abeel T.","year":"2010","unstructured":"T. Abeel, T. Helleputte, Y. Van de Peer, P. Dupont, and Y. Saeys. 2010. Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics 26, 3 (2010), 392\u2013398.","journal-title":"Bioinformatics"},{"issue":"5","key":"e_1_3_2_7_2","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1038\/nbt1203","article-title":"Gene prioritization through genomic data fusion","volume":"24","author":"Aerts Stein","year":"2006","unstructured":"Stein Aerts, Diether Lambrechts, Sunit Maity, Peter Van Loo, Bert Coessens, Frederik De Smet, Leon-Charles Tranchevent, Bart De Moor, Peter Marynen, Bassem Hassan, Peter Carmeliet, and Yves Moreau. 2006. Gene prioritization through genomic data fusion. 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Alwan, Zahraa K. Taha, Sura F. Ismail, Rula A. Hamid, A. A. Zaidan, Osamah Shihab Albahri, B. B. Zaidan, A. H. Alamoodi, and M. A. Alsalem. 2021. IoT-based telemedicine for disease prevention and health promotion: State-of-the-art. J. Netw. Comput. Applic. 173 (2021), 102873.","journal-title":"J. Netw. Comput. Applic."},{"issue":"6769","key":"e_1_3_2_12_2","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1038\/35000501","article-title":"Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling","volume":"403","author":"Alizadeh A. A.","year":"2000","unstructured":"A. A. Alizadeh, M. B. Eisen, R. E. Davis, C. Ma, I. S. Lossos, A. Rosenwald, J. C. Boldrick, H. Sabet, T. Tran, X. Yu, J. I. Powell, L. Yang, G. E. Marti, T. Moore, J. Hudson, L. Lu, D. B. Lewis, R. Tibshirani, G. Sherlock, W. C. Chan, T. C. Greiner, D. D. Weisenburger, J. O. Armitage, R. Warnke, W. Wilson R Levy, M. R. Grever, J. C. Byrd ad D. Botstein, and L. M. Staudt P. O. Brown. 2000. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 6769 (2000), 503\u2013511.","journal-title":"Nature"},{"issue":"12","key":"e_1_3_2_13_2","doi-asserted-by":"crossref","first-page":"6745","DOI":"10.1073\/pnas.96.12.6745","article-title":"Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays","volume":"96","author":"Alon U.","year":"1999","unstructured":"U. Alon, N. Barkai, D. A. Notterman, K. Gish, S. Ybarra, D. Mack, and A. J. Levine. 1999. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Nat. Acad. Sci. 96, 12 (1999), 6745\u20136750.","journal-title":"Proc. Nat. Acad. Sci."},{"issue":"1","key":"e_1_3_2_14_2","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/ng765","article-title":"MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia","volume":"30","author":"Armstrong Scott A.","year":"2001","unstructured":"Scott A. Armstrong, Jane E. Staunton, Lewis B. Silverman, Rob Pieters, Monique L. den Boer, Mark D. Minden, Stephen E. Sallan, Eric S. Lander, Todd R. Golub, and Stanley J. Korsmeyer. 2001. MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. 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In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 5987\u20135990."},{"issue":"4","key":"e_1_3_2_17_2","first-page":"45","article-title":"A computer-aided diagnosis system for breast cancer combining features complementarily and new scheme of SVM classifiers fusion","volume":"8","author":"Azizi N.","year":"2013","unstructured":"N. Azizi, Y. Tlili-Guiassa, and N. Zemmal. 2013. A computer-aided diagnosis system for breast cancer combining features complementarily and new scheme of SVM classifiers fusion. Int. J. Multim. Ubiq. Eng. 8, 4 (2013), 45\u201358.","journal-title":"Int. J. Multim. Ubiq. Eng."},{"key":"e_1_3_2_18_2","volume-title":"BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning","author":"Bacardit Jaume","year":"2006","unstructured":"Jaume Bacardit and Natalio Krasnogor. 2006. BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning. Technical Report. University of Nottingham."},{"issue":"6","key":"e_1_3_2_19_2","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1109\/JBHI.2020.3027680","article-title":"Identification of lncRNA signature associated with pan-cancer prognosis","volume":"25","author":"Bao Guoqing","year":"2021","unstructured":"Guoqing Bao, Ran Xu, Xiuying Wang, Jianxiong Ji, Linlin Wang, Wenjie Li, Qing Zhang, Bin Huang, Anjing Chen, Di Zhang, Beihua Kong, Qifeng Yang, Cunzhong Yuan, Xinyu Wang, Jian Wang, and Xingang Li. 2021. Identification of lncRNA signature associated with pan-cancer prognosis. IEEE J. Biomed. Health Inform. 25, 6 (2021), 2317\u20132328.","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"e_1_3_2_20_2","first-page":"261","article-title":"Machine learning contribution to solve prognostic medical problems","volume":"261","author":"Baronti F.","year":"2006","unstructured":"F. Baronti, A. Micheli, A. Passaro, and A. Starita. 2006. Machine learning contribution to solve prognostic medical problems. Outc. Predict. Cancer 261, 1 (2006), 261\u2013283.","journal-title":"Outc. Predict. Cancer"},{"key":"e_1_3_2_21_2","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/CATCON.2015.7449500","volume-title":"Proceedings of the International Conference on Condition Assessment Techniques in Electrical Systems (CATCON)","author":"Begum S.","year":"2015","unstructured":"S. Begum, D. Chakraborty, and R. Sarkar. 2015. Cancer classification from gene expression based microarray data using SVM ensemble. In Proceedings of the International Conference on Condition Assessment Techniques in Electrical Systems (CATCON). IEEE, 13\u201316."},{"key":"e_1_3_2_22_2","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.envsoft.2016.10.013","article-title":"An overview of the model integration process: From pre-integration assessment to testing","volume":"87","author":"Belete Getachew F.","year":"2017","unstructured":"Getachew F. Belete, Alexey Voinov, and Gerard F. Laniak. 2017. An overview of the model integration process: From pre-integration assessment to testing. Environ. Model. Softw. 87 (2017), 49\u201363.","journal-title":"Environ. Model. Softw."},{"issue":"2","key":"e_1_3_2_23_2","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1002\/pmic.200500192","article-title":"A robust meta-classification strategy for cancer detection from MS data","volume":"6","author":"Bhanot G.","year":"2006","unstructured":"G. Bhanot, G. Alexe, B. Venkataraghavan, and A. J. Levine. 2006. A robust meta-classification strategy for cancer detection from MS data. Proteomics 6, 2 (2006), 592\u2013604.","journal-title":"Proteomics"},{"key":"e_1_3_2_24_2","first-page":"8151","volume-title":"Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","author":"Bountris P.","year":"2015","unstructured":"P. Bountris, M. Haritou, A. Pouliakis, P. Karakitsos, and D. Koutsouris. 2015. A decision support system based on an ensemble of random forests for improving the management of women with abnormal findings at cervical cancer screening. In Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 8151\u20138156."},{"issue":"2","key":"e_1_3_2_25_2","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman L.","year":"1996","unstructured":"L. Breiman. 1996. Bagging predictors. Mach. Learn. 24, 2 (1996), 123\u2013140.","journal-title":"Mach. 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Domain knowledge powered deep learning for breast cancer diagnosis based on contrast-enhanced ultrasound videos. IEEE Trans. Med. Imag. 40, 9 (2021), 2439\u20132451.","journal-title":"IEEE Trans. Med. Imag."},{"issue":"2","key":"e_1_3_2_28_2","first-page":"W305\u2013W311","article-title":"ToppGene Suite for gene list enrichment analysis and candidate gene prioritization","volume":"37","author":"Chen Jing","year":"2009","unstructured":"Jing Chen, Eric E. Bardes, Bruce J. Aronow, and Anil G. Jegga. 2009. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37, suppl2 (2009), W305\u2013W311.","journal-title":"Nucleic Acids Res."},{"issue":"4","key":"e_1_3_2_29_2","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1109\/TMI.2020.3021387","article-title":"Pathomic fusion: An integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis","volume":"41","author":"Chen Richard J.","year":"2020","unstructured":"Richard J. Chen, Ming Y. Lu, Jingwen Wang, Drew F. K. Williamson, Scott J. Rodig, Neal I. Lindeman, and Faisal Mahmood. 2020. Pathomic fusion: An integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Trans. Med. Imag. 41, 4 (2020), 757\u2013770.","journal-title":"IEEE Trans. Med. Imag."},{"issue":"2","key":"e_1_3_2_30_2","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1109\/72.80341","article-title":"Orthogonal least squares learning algorithm for radial basis function networks","volume":"2","author":"Chen S.","year":"1991","unstructured":"S. Chen, C. F. N. Cowan, and P. M. Grant. 1991. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Netw. 2, 2 (1991), 302\u2013309.","journal-title":"IEEE Trans. Neural Netw."},{"issue":"6","key":"e_1_3_2_31_2","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1091\/mbc.02-02-0023","article-title":"Gene expression patterns in human liver cancers","volume":"13","author":"Chen Xin","year":"2002","unstructured":"Xin Chen, Siu Tim Cheung, Samuel So, Sheung Tat Fan, Christopher Barry, John Higgins, Kin-Man Lai, Jiafu Ji, Sandrine Dudoit, Irene O. L. Ng, Matt van de Rijn, David Botstein, and Patrick O. Brown. 2002. Gene expression patterns in human liver cancers. Molecul. Biol. Cell 13, 6 (2002), 1929\u20131939.","journal-title":"Molecul. Biol. Cell"},{"issue":"4","key":"e_1_3_2_32_2","doi-asserted-by":"crossref","first-page":"3190","DOI":"10.1109\/TITS.2020.3032758","article-title":"Acting as a decision maker: Traffic-condition-aware ensemble learning for traffic flow prediction","volume":"23","author":"Chen Yuanyuan","year":"2020","unstructured":"Yuanyuan Chen, Hongyu Chen, Peijun Ye, Yisheng Lv, and Fei-Yue Wang. 2020. Acting as a decision maker: Traffic-condition-aware ensemble learning for traffic flow prediction. IEEE Trans. Intell. Transport. Syst. 23, 4 (2020), 3190\u20133200.","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"issue":"5","key":"e_1_3_2_33_2","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1002\/bimj.201500075","article-title":"Ensemble survival trees for identifying subpopulations in personalized medicine","volume":"58","author":"Chen Y.","year":"2016","unstructured":"Y. Chen and J. J. Chen. 2016. Ensemble survival trees for identifying subpopulations in personalized medicine. Biomet. J. 58, 5 (2016), 1151\u20131163.","journal-title":"Biomet. J."},{"issue":"4","key":"e_1_3_2_34_2","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1016\/j.asoc.2008.01.006","article-title":"A novel ensemble of classifiers for microarray data classification","volume":"8","author":"Chen Y.","year":"2008","unstructured":"Y. Chen and Y. Zhao. 2008. A novel ensemble of classifiers for microarray data classification. Appl. Soft Comput. 8, 4 (2008), 1664\u20131669.","journal-title":"Appl. Soft Comput."},{"key":"e_1_3_2_35_2","first-page":"289","volume-title":"Applications of Intelligent Optimization in Biology and Medicine:Current Trends and Open Problems","author":"Cheriguene S.","year":"2015","unstructured":"S. Cheriguene, N. Azizi, N. Zemmal, N. Dey, H. Djellali, and N. Farah. 2015. Optimized tumor breast cancer classification using combining random subspace and static classifiers selection paradigms. In Applications of Intelligent Optimization in Biology and Medicine:Current Trends and Open Problems. Springer, Switzerland, 289\u2013307."},{"issue":"6","key":"e_1_3_2_36_2","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/j.ccr.2006.10.009","article-title":"Genomic and transcriptional aberrations linked to breast cancer pathophysiologies","volume":"10","author":"Chin Koei","year":"2006","unstructured":"Koei Chin, Sandy DeVries, Jane Fridlyand, Paul T. Spellman, Ritu Roydasgupta, Wen-Lin Kuo, Anna Lapuk, Richard M. Neve, Zuwei Qian, Tom Ryder, Fanqing Chen, Heidi Feiler, Taku Tokuyasu, Chris Kingsley, Shanaz Dairkee, Zhenhang Meng, Karen Chew, Daniel Pinkel, Ajay Jain, Britt Marie Ljung, Laura Esserman, Donna G. Albertson, Frederic M. Waldman, and Joe W. Gray. 2006. Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. Cancer Cell 10, 6 (2006), 529\u2013541.","journal-title":"Cancer Cell"},{"issue":"1","key":"e_1_3_2_37_2","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","article-title":"Regression models and life tables (with discussion)","volume":"34","author":"Cox D. R.","year":"1972","unstructured":"D. R. Cox. 1972. Regression models and life tables (with discussion). J. Roy. Statist. Societ. 34, 1 (1972), 187\u2013220.","journal-title":"J. Roy. Statist. Societ."},{"issue":"9","key":"e_1_3_2_38_2","first-page":"1","article-title":"Network and data integration for biomarker signature discovery via network smoothed t-statistics","volume":"8","author":"Cun Y.","year":"2013","unstructured":"Y. Cun and H. Fr\u00f6hlich. 2013. Network and data integration for biomarker signature discovery via network smoothed t-statistics. PLoS One 8, 9 (2013), 1\u20139.","journal-title":"PLoS One"},{"issue":"6","key":"e_1_3_2_39_2","first-page":"1","article-title":"Brain tumor segmentation based on improved convolutional neural network in combination with non-quantifiable local texture feature","volume":"43","author":"Deng Wu","year":"2019","unstructured":"Wu Deng, Qinke Shi, Kai Luo, Yi Yang, and Ning Ning. 2019. Brain tumor segmentation based on improved convolutional neural network in combination with non-quantifiable local texture feature. J. Med. Syst. 43, 6 (2019), 1\u20139.","journal-title":"J. Med. 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Cancer Res."},{"issue":"3","key":"e_1_3_2_41_2","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1504\/IJCBDD.2008.021422","article-title":"An ensemble machine learning approach to predict survival in breast cancer","volume":"1","author":"Djebbari A.","year":"2008","unstructured":"A. Djebbari, Z. Liu, S. Phan, and F. Famili. 2008. An ensemble machine learning approach to predict survival in breast cancer. Int. J. Computat. Biol. Drug Des. 1, 3 (2008), 275\u2013294.","journal-title":"Int. J. Computat. Biol. Drug Des."},{"key":"e_1_3_2_42_2","first-page":"332","volume-title":"Proceedings of the International Workshop on Power and Timing Modeling, Optimization and Simulation","author":"Drosos C.","year":"2004","unstructured":"C. Drosos, L. Bisdounis, D. Metafas, S. Blionas, and A. Tatsaki. 2004. A multi-level validation methodology for wireless network applications. In Proceedings of the International Workshop on Power and Timing Modeling, Optimization and Simulation. Springer, 332\u2013341."},{"key":"e_1_3_2_43_2","volume-title":"Breast Cancer Histopathological Database (BreakHis)","author":"Imagem Laboratorio Visao Robotica e","year":"2022","unstructured":"Laboratorio Visao Robotica e Imagem. 2022. Breast Cancer Histopathological Database (BreakHis). Universidade Federal do Parana. Retrieved from ftp:\/\/ftp.cs.wisc.edu\/math-prog\/cpo-dataset\/machine-learn\/WDBC\/."},{"issue":"12","key":"e_1_3_2_44_2","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1016\/j.ejca.2004.02.025","article-title":"\u201cGood Old\u201d clinical markers have similar power in breast cancer prognosis as microarray gene expression profilers","volume":"40","author":"Ed\u00e9n P.","year":"2004","unstructured":"P. Ed\u00e9n, C. Ritz, Roseand Fern\u00f6 Ma, and C. Peterson. 2004. \u201cGood Old\u201d clinical markers have similar power in breast cancer prognosis as microarray gene expression profilers. Eur. J. 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ACM, New York, NY, 1\u20138."},{"issue":"6","key":"e_1_3_2_47_2","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1086\/504300","article-title":"Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes","volume":"78","author":"Franke L.","year":"2006","unstructured":"L. Franke, H. Van Bakel, L. Fokkens, E. D. De Jong, M. Egmont-Petersen, and C. Wijmenga. 2006. Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Amer. J. Hum. Genet. 78, 6 (2006), 1011\u20131025.","journal-title":"Amer. J. Hum. Genet."},{"issue":"14","key":"e_1_3_2_48_2","doi-asserted-by":"crossref","first-page":"e184\u2013e190","DOI":"10.1093\/bioinformatics\/btl230","article-title":"Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks","volume":"22","author":"Gevaert O.","year":"2006","unstructured":"O. Gevaert, F. De Smet, D. 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Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomfield, and E. S. Lander. 1999. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 5439 (1999), 531\u2013537.","journal-title":"Science"},{"key":"e_1_3_2_52_2","first-page":"169","volume-title":"Proceedings of the International Conference on Artificial Intelligence and Soft Computing","author":"Grzesiak-Kopec K.","year":"2016","unstructured":"K. Grzesiak-Kopec, M. Ogorzalek, and L. Nowak. 2016. Computational classification of melanocytic skin lesions. In Proceedings of the International Conference on Artificial Intelligence and Soft Computing. Springer, 169\u2013178."},{"key":"e_1_3_2_53_2","doi-asserted-by":"crossref","first-page":"104806","DOI":"10.1016\/j.compbiomed.2021.104806","article-title":"A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning","volume":"137","author":"Gu Yu","year":"2021","unstructured":"Yu Gu, Jingqian Chi, Jiaqi Liu, Lidong Yang, Baohua Zhang, Dahua Yu, Ying Zhao, and Xiaoqi Lu. 2021. A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning. Comput. Biol. Med. 137 (2021), 104806.","journal-title":"Comput. Biol. Med."},{"key":"e_1_3_2_54_2","first-page":"433","volume-title":"Proceedings of the Conference on Computers in Cardiology","author":"Guvenir H. A.","year":"1997","unstructured":"H. A. Guvenir, B. Acar, G. Demiroz, and A. Cekin. 1997. A supervised machine learning algorithm for arrhythmia analysis. In Proceedings of the Conference on Computers in Cardiology. 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Retrieved from https:\/\/archive.ics.uci.edu\/ml\/machine-learning-databases\/thyroiddisease\/HELLO."},{"issue":"21","key":"e_1_3_2_62_2","article-title":"Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer","volume":"66","author":"Ivshina Anna V.","year":"2006","unstructured":"Anna V. Ivshina, Joshy George, Oleg Senko, Benjamin Mow, Thomas C. Putti, Johanna Smeds, Thomas Lindahl, Yudi Pawitan, Per Hall, Hans Nordgren, John E. L. Wong, Edison T. Liu, Jonas Bergh, Vladimir A. Kuznetsov, and Lance D. Miller. 2006. Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer. Cancer Res. 66, 21 (2006), 10292\u2013301.","journal-title":"Cancer Res."},{"issue":"1","key":"e_1_3_2_63_2","first-page":"933","article-title":"An efficient boosting algorithm for combining preferences","volume":"4","author":"Iyer R. D.","year":"1999","unstructured":"R. D. Iyer. 1999. An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4, 1 (1999), 933\u2013969.","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"e_1_3_2_64_2","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1162\/neco.1991.3.1.79","article-title":"Adaptive mixtures of local experts","volume":"3","author":"Jacobs R. A.","year":"1991","unstructured":"R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton. 1991. Adaptive mixtures of local experts. Neural Computat. 3, 1 (1991), 79\u201387.","journal-title":"Neural Computat."},{"key":"e_1_3_2_65_2","first-page":"8","volume-title":"Proceedings of the International Conference in Swarm Intelligence","author":"Janghel R. R.","year":"2014","unstructured":"R. R. Janghel, A. Shukla, S. Sharma, and A. V. Gnaneswar. 2014. Evolutionary ensemble model for breast cancer classification. In Proceedings of the International Conference in Swarm Intelligence. Springer, 8\u201316."},{"issue":"1","key":"e_1_3_2_66_2","doi-asserted-by":"crossref","first-page":"101570","DOI":"10.1016\/j.jocs.2022.101570","article-title":"BSense: A parallel Bayesian hyperparameter optimized Stacked ensemble model for breast cancer survival prediction","volume":"60","author":"Kaur Parampreet","year":"2022","unstructured":"Parampreet Kaur, Ashima Singh, and Inderveer Chana. 2022. BSense: A parallel Bayesian hyperparameter optimized Stacked ensemble model for breast cancer survival prediction. J. Computat. Sci. 60, 1 (2022), 101570.","journal-title":"J. Computat. Sci."},{"issue":"4","key":"e_1_3_2_67_2","first-page":"577","article-title":"Mathematics without numbers","volume":"88","author":"Kemeny J. G.","year":"1959","unstructured":"J. G. Kemeny. 1959. Mathematics without numbers. Daedalus 88, 4 (1959), 577\u2013591.","journal-title":"Daedalus"},{"issue":"5","key":"e_1_3_2_68_2","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/TPAMI.2007.1068","article-title":"Twin support vector machines for pattern classification","volume":"29","author":"Khemchandani R.","year":"2007","unstructured":"R. Khemchandani and S. Chandra. 2007. Twin support vector machines for pattern classification. IEEE Trans. Pattern Anal. Mach. Intell. 29, 5 (2007), 905\u2013910.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7","key":"e_1_3_2_69_2","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1093\/bioinformatics\/btu766","article-title":"HyDRA: Gene prioritization via hybrid distance-score rank aggregation","volume":"31","author":"Kim M.","year":"2015","unstructured":"M. Kim, F. Farnoud, and O. Milenkovic. 2015. HyDRA: Gene prioritization via hybrid distance-score rank aggregation. 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J."},{"issue":"1","key":"e_1_3_2_73_2","first-page":"8","article-title":"Artificial intelligence methods application in liver diseases classification from CT images 1 introduction 2 methods","volume":"13","author":"Kourou K.","year":"2015","unstructured":"K. Kourou, T. P. Exarchos, M. V. Karamouzis K. P. Exarchos and, and D. I. Fotiadis. 2015. Artificial intelligence methods application in liver diseases classification from CT images 1 introduction 2 methods. Computat. Struct. Biotechnol. J. 13, 1 (2015), 8\u201317.","journal-title":"Computat. Struct. Biotechnol. J."},{"issue":"2","key":"e_1_3_2_74_2","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1089\/1066527041410463","article-title":"Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data","volume":"11","author":"Krishnapuram B.","year":"2004","unstructured":"B. Krishnapuram, L. Carin, and A. J. Hartemink. 2004. Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data. J. Computat. Biol. 11, 2-3 (2004), 227\u2013242.","journal-title":"J. Computat. Biol."},{"issue":"2","key":"e_1_3_2_75_2","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s10115-012-0586-6","article-title":"A weighted voting framework for classifiers ensembles","volume":"38","author":"Kuncheva L. I.","year":"2014","unstructured":"L. I. Kuncheva and J. J. Rodr\u00edguez. 2014. A weighted voting framework for classifiers ensembles. Knowl. Inf. Syst. 38, 2 (2014), 259\u2013275.","journal-title":"Knowl. Inf. Syst."},{"issue":"2","key":"e_1_3_2_76_2","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1023\/A:1022859003006","article-title":"Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy","volume":"51","author":"Kuncheva L. I.","year":"2003","unstructured":"L. I. Kuncheva and C. J. Whitaker. 2003. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51, 2 (2003), 181\u2013207.","journal-title":"Mach. Learn."},{"issue":"1","key":"e_1_3_2_77_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2012.02.009","article-title":"Optimizations of the na\u00efve-Bayes classifier for the prognosis of B-chronic lymphocytic leukemia incorporating flow cytometry data","volume":"108","author":"Lakoumentas J.","year":"2012","unstructured":"J. Lakoumentas, J. Drakos, M. Karakantza, G. Sakellaropoulos, V. Megalooikonomou, and G. Nikiforidis. 2012. Optimizations of the na\u00efve-Bayes classifier for the prognosis of B-chronic lymphocytic leukemia incorporating flow cytometry data. Comput. Meth. Prog. Biomed. 108, 1 (2012), 158\u201367.","journal-title":"Comput. Meth. Prog. Biomed."},{"key":"e_1_3_2_78_2","first-page":"1","volume-title":"Proceedings of the SPIE Medical Imaging Conference","author":"Lederman D.","year":"2011","unstructured":"D. Lederman, X. Wang, B. Zheng, J. H. Sumkin, M. Tublin, and D. Gur. 2011. Fusion of classifiers for REIS-based detection of suspicious breast lesions. In Proceedings of the SPIE Medical Imaging Conference. SPIE, 1\u20138."},{"key":"e_1_3_2_79_2","first-page":"3507","volume-title":"Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","author":"Lee P.-F.","year":"2013","unstructured":"P.-F. Lee and V.-W. Soo. 2013. An ensemble rank learning approach for gene prioritization. In Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 3507\u20133510."},{"issue":"1","key":"e_1_3_2_80_2","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.eswa.2016.05.020","article-title":"Fusion of FISH image analysis methods of HER2 status determination in breast cancer","volume":"61","author":"Les T.","year":"2016","unstructured":"T. Les, T. Markiewicz, S. Osowski, W. Kozlowski, and M. Jesiotr. 2016. Fusion of FISH image analysis methods of HER2 status determination in breast cancer. Expert Syst. Applic. 61, 1 (2016), 78\u201385.","journal-title":"Expert Syst. Applic."},{"key":"e_1_3_2_81_2","first-page":"178","volume-title":"Proceedings of the 22nd International Conference on Pattern Recognition","author":"Li L.","year":"2014","unstructured":"L. Li, Z. Yu, J. Liu, J. You, H.-S. Wong, and G. Han. 2014. Multi-view based AdaBoost classifier ensemble for class prediction from gene expression profiles. In Proceedings of the 22nd International Conference on Pattern Recognition. IEEE, 178\u2013183."},{"issue":"11","key":"e_1_3_2_82_2","first-page":"3403","article-title":"Computer-aided cervical cancer diagnosis using time-lapsed colposcopic images","volume":"39","author":"Li Yuexiang","year":"2021","unstructured":"Yuexiang Li, Jiawei Chen, Peng Xue, Chao Tang, Jia Chang, Chunyan Chu, Kai Ma, Qing Li, Yefeng Zheng, and Youlin Qiao. 2021. Computer-aided cervical cancer diagnosis using time-lapsed colposcopic images. IEEE Trans. Med. Imag. 39, 11 (2021), 3403\u20133415.","journal-title":"IEEE Trans. Med. Imag."},{"issue":"1","key":"e_1_3_2_83_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-5-1","article-title":"A combinational feature selection and ensemble neural network method for classification of gene expression data","volume":"5","author":"Liu B.","year":"2004","unstructured":"B. Liu, Q. Cui, T. Jiang, and S. Ma. 2004. A combinational feature selection and ensemble neural network method for classification of gene expression data. BMC Bioinform. 5, 1 (2004), 1\u201312.","journal-title":"BMC Bioinform."},{"issue":"10","key":"e_1_3_2_84_2","doi-asserted-by":"crossref","first-page":"2735","DOI":"10.1109\/TBME.2020.2969839","article-title":"Robust collaborative clustering of subjects and radiomic features for cancer prognosis","volume":"67","author":"Liu Hangfan","year":"2020","unstructured":"Hangfan Liu, Hongming Li, Mohamad Habes, Yuemeng Li, Pamela Boimel, James Janopaul-Naylor, Ying Xiao, Edgar Ben-Josef, and Yong Fan. 2020. Robust collaborative clustering of subjects and radiomic features for cancer prognosis. IEEE Trans. Biomed. Eng. 67, 10 (2020), 2735\u20132744.","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"1","key":"e_1_3_2_85_2","first-page":"1","article-title":"Evaluation and integration of cancer gene classifiers: Identification and ranking of plausible drivers","volume":"5","author":"Liu Y.","year":"2015","unstructured":"Y. Liu, F. Tian, Z. Hu, and C. DeLisi. 2015. Evaluation and integration of cancer gene classifiers: Identification and ranking of plausible drivers. Sci. Rep. 5, 1 (2015), 1\u201315.","journal-title":"Sci. Rep."},{"issue":"1","key":"e_1_3_2_86_2","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1002\/widm.8","article-title":"Classification and regression trees","volume":"1","author":"Loh Wei-Yin","year":"2011","unstructured":"Wei-Yin Loh. 2011. Classification and regression trees. Data Mining Knowl. Discov. 1, 1 (2011), 14\u201323.","journal-title":"Data Mining Knowl. Discov."},{"issue":"25","key":"e_1_3_2_87_2","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1056\/NEJMoa021967","article-title":"A gene-expression signature as a predictor of survival in breast cancer","volume":"347","author":"Vijver M. D. Marc J. van de","year":"2002","unstructured":"M. D. Marc J. van de Vijver, Yudong D. He, Laura J. van\u2019t Veer, Hongyue Dai, Augustinus A. M. Hart, Dorien W. Voskuil, George J. Schreiber, Johannes L. Peterse, Chris Roberts, Matthew J. Marton, Mark Parrish, Douwe Atsma, Anke Witteveen, Annuska Glas, Leonie Delahaye, Tony van der Velde, Harry Bartelink, Sjoerd Rodenhuis, Emiel T. Rutgers, Stephen H. Friend, and Rene Bernards. 2002. A gene-expression signature as a predictor of survival in breast cancer. New Eng. J. Med. 347, 25 (2002), 1999\u20132009.","journal-title":"New Eng. J. Med."},{"key":"e_1_3_2_88_2","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.jbi.2018.01.005","article-title":"Computer-assisted decision support system in pulmonary cancer detection and stage classification on CT images","volume":"79","author":"Masood Anum","year":"2018","unstructured":"Anum Masood, Bin Sheng, Ping Li, Xuhong Hou, Xiaoer Wei, Jing Qin, and Dagan Feng. 2018. Computer-assisted decision support system in pulmonary cancer detection and stage classification on CT images. J. Biomed. Inform. 79 (2018), 117\u2013128.","journal-title":"J. Biomed. 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Genom."},{"issue":"1","key":"e_1_3_2_91_2","first-page":"1","article-title":"A stacking ensemble deep learning approach to cancer type classification based on TCGA data","volume":"11","author":"Mohammed Mohanad","year":"2021","unstructured":"Mohanad Mohammed, Henry Mwambi, Innocent B. Mboya, Murtada K. Elbashir, and Bernard Omolo. 2021. A stacking ensemble deep learning approach to cancer type classification based on TCGA data. Sci. Rep. 11, 1 (2021), 1\u201322.","journal-title":"Sci. Rep."},{"issue":"21","key":"e_1_3_2_92_2","doi-asserted-by":"crossref","first-page":"4247","DOI":"10.1093\/bioinformatics\/btz233","article-title":"A Bayesian model integration for mutation calling through data partitioning","volume":"35","author":"Moriyama T.","year":"2019","unstructured":"T. Moriyama, S. Imoto, S. Hayashi, Y. Shiraishi, S. Miyano, and R. Yamaguchi. 2019. A Bayesian model integration for mutation calling through data partitioning. 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R.","year":"1986","unstructured":"J. R. Quinlan. 1986. Induction of Decision Trees. Mach. Learn. 8, 1 (1986), 81\u2013106.","journal-title":"Mach. Learn."},{"issue":"14","key":"e_1_3_2_106_2","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar Alvin","year":"2019","unstructured":"Alvin Rajkomar, Jeffrey Dean, and Isaac Kohane. 2019. Machine learning in medicine. New Eng. J. Med. 380, 14 (2019), 1347\u20131358.","journal-title":"New Eng. J. Med."},{"issue":"1","key":"e_1_3_2_107_2","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.asoc.2014.01.002","article-title":"A novel ensemble of classifiers that use biological relevant gene sets for microarray classification","volume":"17","author":"Reboiro-Jato M.","year":"2014","unstructured":"M. Reboiro-Jato, F. D\u00edaz, D. Glez-Pe\u00f1a, and F. Fdez-Riverola. 2014. 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Optimizing molecular signatures for predicting prostate cancer recurrence. Prostate 69, 10 (2009), 1119\u20131127.","journal-title":"Prostate"},{"issue":"5","key":"e_1_3_2_118_2","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/j.patrec.2011.10.019","article-title":"Multilabel classification using heterogeneous ensemble of multi-label classifiers","volume":"33","author":"Tahir M. A.","year":"2012","unstructured":"M. A. Tahir, J. Kittler, and A. Bouridane. 2012. Multilabel classification using heterogeneous ensemble of multi-label classifiers. Pattern Recog. Lett. 33, 5 (2012), 513\u2013523.","journal-title":"Pattern Recog. Lett."},{"key":"e_1_3_2_119_2","doi-asserted-by":"crossref","unstructured":"Barry S. Taylor Nikolaus Schultz Haley Hieronymus Anuradha Gopalan Brett S. Carver Yonghong Xiao Vivek K. Arora Poorvi Kaushik Ethan Cerami Boris Reva Yevgeniy Antipin Nicholas Mitsiades Thomas Landers Igor Dolgalev John E. Major Manda Wilson Nicholas D. Socci Alex E. Lash Adriana Heguy James A. Eastham Howard I. Scher Victor E. Reuter Peter T. Scardino Chris Sander Charles L. Sawyers and William L. Geral. 2010. Integrative genomic profiling of human prostate cancer. Cancer Cell 18 1 (2010) 11\u201322.","DOI":"10.1016\/j.ccr.2010.05.026"},{"key":"e_1_3_2_120_2","volume-title":"A Package for Survival Analysis in S. version 2.38 (2015)","author":"Therneau Terry M.","year":"2022","unstructured":"Terry M. Therneau, Thomas Lumley, Atkinson Elizabeth, and Crowson Cynthia. 2022. A Package for Survival Analysis in S. version 2.38 (2015). CRAN. Retrieved from https:\/\/CRAN.R-project.org\/package=survival."},{"issue":"2","key":"e_1_3_2_121_2","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.media.2012.10.004","article-title":"Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI\/MRS","volume":"17","author":"Tiwari P.","year":"2013","unstructured":"P. Tiwari, J. Kurhanewicz, and A. Madabhushi. 2013. Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI\/MRS. Med. Image Anal. 17, 2 (2013), 219\u2013235.","journal-title":"Med. Image Anal."},{"issue":"2","key":"e_1_3_2_122_2","doi-asserted-by":"crossref","first-page":"2452","DOI":"10.1016\/j.asoc.2010.10.001","article-title":"Predicting stock returns by classifier ensembles","volume":"11","author":"Tsai C.-F.","year":"2011","unstructured":"C.-F. Tsai, Y.-C. Lin, D. C. Yen, and Y.-M. Chen. 2011. Predicting stock returns by classifier ensembles. Appl. Soft Comput. 11, 2 (2011), 2452\u20132459.","journal-title":"Appl. Soft Comput."},{"issue":"9","key":"e_1_3_2_123_2","doi-asserted-by":"crossref","first-page":"5116","DOI":"10.1073\/pnas.091062498","article-title":"Significance analysis of microarrays applied to the ionizing radiation response","volume":"98","author":"Tusher Virginia Goss","year":"2001","unstructured":"Virginia Goss Tusher, Robert Tibshirani, and Gilbert Chu. 2001. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Nat. Acad. Sci. United States Amer. 98, 9 (2001), 5116\u20135121.","journal-title":"Proc. Nat. Acad. Sci. United States Amer."},{"key":"e_1_3_2_124_2","volume-title":"Education: Citation Databases","author":"University Istanbul Kultur","year":"2022","unstructured":"Istanbul Kultur University. 2022. Education: Citation Databases. IKU. Retrieved from https:\/\/iku.libguides.com\/education\/citationdatabases."},{"issue":"6871","key":"e_1_3_2_125_2","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1038\/415530a","article-title":"Gene expression profiling predicts clinical outcome of breast cancer","volume":"415","author":"Veer Laura J. van \u2019t","year":"2002","unstructured":"Laura J. van \u2019t Veer, Hongyue Dai, Marc J. van de Vijver, Yudong D. He, Augustinus A. M. Hart, Mao Mao, Hans L. Peterse, Karin van der Kooy, Matthew J. Marton, Anke T. Witteveen, George J. Schreiber, Ron M. Kerkhoven, Chris Roberts, Peter S. Linsley, Rene Bernards, and Stephen H. Friend. 2002. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 6871 (2002), 530\u2013536.","journal-title":"Nature"},{"key":"e_1_3_2_126_2","first-page":"1","article-title":"Ensemble feature selection for stable biomarker identification and cancer classification from microarray expression data","volume":"142","author":"Wang Aiguo","year":"2022","unstructured":"Aiguo Wang, Huanchen Liu, Jing Yang, and Guilin Chen. 2022. Ensemble feature selection for stable biomarker identification and cancer classification from microarray expression data. Comput. Biol. Med. 142, C (2022), 1\u201312.","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"e_1_3_2_127_2","first-page":"206","article-title":"Identification of functional modules by integration of multiple data sources using a Bayesian network classifier","volume":"7","author":"Wang J.","year":"2014","unstructured":"J. Wang, Y. Zuo, L. Liu, Y. Man, M. G. Tadesse, and H. W. Ressom. 2014. Identification of functional modules by integration of multiple data sources using a Bayesian network classifier. Circul.: Cardiovasc. Genet. 7, 2 (2014), 206\u2013217.","journal-title":"Circul.: Cardiovasc. Genet."},{"issue":"9460","key":"e_1_3_2_128_2","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1016\/S0140-6736(05)17947-1","article-title":"Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer","volume":"365","author":"Wang Yixin","year":"2005","unstructured":"Yixin Wang, Jan G. M. Klijn, Yi Zhang, Anieta M. Sieuwerts, Maxime P. Look, Fei Yang, Dmitri Talantov, Mieke Timmermans, Marion E. Meijer van Gelder, Jack Yu, Tim Jatkoe, Els M. J. J. Berns, David Atkins, and John A. Foekens. 2005. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365, 9460 (2005), 671\u2013679.","journal-title":"Lancet"},{"key":"e_1_3_2_129_2","first-page":"702","volume-title":"Proceedings of the International Joint Conferences on Artificial Intelligence","author":"Webb G. I.","year":"1999","unstructured":"G. I. Webb. 1999. Decision tree grafting from the all-tests-but-one partition. In Proceedings of the International Joint Conferences on Artificial Intelligence. IJCAI, 702\u2013707."},{"issue":"6","key":"e_1_3_2_130_2","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1093\/bioinformatics\/btq044","article-title":"Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning","volume":"26","author":"Wu C.-C.","year":"2010","unstructured":"C.-C. Wu, S. Asgharzadeh, T. J. Triche, and D. Z. D\u2019Argenio. 2010. Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning. 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In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Workshops. IEEE, 1\u20135."},{"issue":"1","key":"e_1_3_2_133_2","first-page":"1","article-title":"A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data","volume":"11","author":"Yang P.","year":"2010","unstructured":"P. Yang, B. B. Zhou, Z. Zhang, and A. Y. Zomaya. 2010. A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data. BMC Bioinform. 11, 1 (2010), 1\u201312.","journal-title":"BMC Bioinform."},{"issue":"1","key":"e_1_3_2_134_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-13-12","article-title":"Core module biomarker identification with network exploration for breast cancer metastasis","volume":"13","author":"Yang R.","year":"2012","unstructured":"R. Yang, B. J. Daigle, L. R. Petzold, and F. J. Doyle. 2012. 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In Proceedings of the 1st International Workshop on Multiple Classifier Systems. Springer, 1\u201315."},{"key":"e_1_3_2_137_2","doi-asserted-by":"crossref","unstructured":"P. Zakeri S. Elshal and Y. Moreau. 2015. Gene prioritization through geometric-inspired kernel data fusion. International Conference on Bioinformatics and Biomedicine (BIBM\u201915) IEEE Washington DC 1559\u20131565.","DOI":"10.1109\/BIBM.2015.7359908"},{"issue":"2","key":"e_1_3_2_138_2","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.artmed.2009.07.008","article-title":"Mixture classification model based on clinical markers for breast cancer prognosis","volume":"48","author":"Zeng T.","year":"2010","unstructured":"T. Zeng and J. Liu. 2010. Mixture classification model based on clinical markers for breast cancer prognosis. Artif. Intell. Med. 48, 2 (2010), 129\u2013137.","journal-title":"Artif. Intell. 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Thomas, Katsuhiko Naoki, Christine Ladd-Acosta, Ni Liu, Melania Pintilie, Sandy Der, Lesley Seymour, Igor Jurisica, Frances A. Shepherd, and Ming-Sound Tsao. 2010. Prognostic and predictive gene signature for adjuvant chemotherapy in resected non\u2013small-cell lung cancer. J. Clinic. Oncol. 28, 29 (2010), 4417\u20134424.","journal-title":"J. Clinic. 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