Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data

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    Lin, Chieh, Jain, Siddhartha, Kim, Hannah, Bar-Joseph, Ziv · 2017 · Nucleic Acids Research
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    Szeman Ruby Chan, William Vermi, Jingqin Rosy Luo, Laura Lucini, Charles G. Rickert, Amy M. Fowler, Silvia Lonardi, Cora D. Arthur, Larry Jt Young, David E. Levy et al. · 2012 · Breast Cancer Research
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    Wei Wang, Raju Kandimalla, Hao Huang, Lina Zhu, Ying Li, Feng Gao, Ajay Goel, Xin Wang · 2018 · Seminars in Cancer Biology
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    Clare Stirzaker, Elena Zotenko, Jenny Z. Song, Wenjia Qu, Shalima S. Nair, Warwick J. Locke, Andrew M. Stone, Nicola J. Armstong, Mark D. Robinson, Alexander Dobrovic et al. · 2015 · Nature Communications
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    Zhihua Liu, Yahui Zhao, Pengzhou Kong, Yuhao Liu, Jing Huang, Enwei Xu, Wenqing Wei, Guangyu Li, Xiaolong Cheng, Liyan Xue et al. · 2022 · Cancer Cell
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    Anne Vincent‐Salomon, Nadège Gruel, Carlo Lucchesi, Gaëtan Marie MacGrogan, R. Dendale, Brigitte Sigal‐Zafrani, Michel Longy, Virginie Raynal, Gaëlle Pierron, Isabelle de Mascarel et al. · 2007 · Breast Cancer Research
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    Zhiwen Yu, Peinan Luo, Jane You, Hau−San Wong, HARETON K. N. LEUNG, Si Wu, Jun Zhang, Guoqiang Han · 2015 · IEEE Transactions on Knowledge and Data Engineering
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    Liwei Zhang, Lee H. Chen, Hong Wan, Rui Ryan Yang, Zhaohui Wang, Jie Feng, Shaohua Yang, Siân Jones, Sizhen Wang, Weixin Zhou et al. · 2014 · Nature Genetics
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    Rich Caruana, M.E. El-Hawary, Nam Hoang Nguyen, Casey Smith · 2006 · Proceedings
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    Keyvan Golalipour, Ebrahim Akbari, Seyed Saeed Hamidi, Malrey Lee, Rasul Enayatifar · 2021 · Engineering Applications of Artificial Intelligence
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    Nora K. Speicher, Nico Pfeifer · 2015 · Bioinformatics
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    Sandra Lai-Kwan Ng, Sara Masarone, David Watson, Michael R. Barnes · 2023 · Cell and Tissue Research
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    Mülleder, Michael, Calvani, Enrica, Alam, Mohammad Tauqeer, Wang, Richard Kangda, Eckerstorfer, Florian, Zelezniak, Aleksej, Ralser, Markus · 2016 · Cell
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    Tin Nguyen, Rebecca Tagett, Diana Diaz, Sorin Drăghici · 2017 · Genome Research
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    Zhiwen Yu, Hau−San Wong, Hongqiang Wang · 2007 · Bioinformatics
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    Ido Priness, Oded Maimon, Irad Ben‐Gal · 2007 · BMC Bioinformatics
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    Chiara Ambrogio, Gonzalo Goméz-López, Mattia Falcone, August Vidal, Ernest Nadal, Nicola Crosetto, Rafael B Blasco, Pablo J. Fernández-Marcos, Montse Sánchez‐Céspedes, Xiaomei Ren et al. · 2016 · Nature Medicine
50. Uncoverning Groups via Heterogeneous Interaction Analysis
    Lei Tang, Xufei Wang, Huan Liu · 2009 · Proceedings
51. Cluster ensembles: A survey of approaches with recent extensions and applications
    Tossapon Boongoen, Natthakan Iam-On · 2018 · Computer Science Review
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    Presha Rajbhandari, Gonzalo López, Claudia Capdevila, Beatrice Salvatori, Jiyang Yu, Ruth Rodriguez Barrueco, Daniel Martínez, Mark Yarmarkovich, Nina Weichert‐Leahey, Brian J. Abraham et al. · 2018 · Cancer Discovery
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    Juan J. Fernandez-Tajes, Angel Soto‐Hermida, Maria Eugenia Vázquez-Mosquera, Estefanía Cortés-Pereira, Alejandro Mosquera, Mercedes Fernández-Moreno, Natividad Oreiro, C. Fernández-López, José Luís Fernández, Ignacio Rego‐Pérez et al. · 2013 · Annals of the Rheumatic Diseases
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    Daniel R. Lewis, Amy L. Olex, Stacey R. Lundy, William H. Turkett, Jacquelyn S. Fetrow, Gloria K. Muday · 2013 · The Plant Cell
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    Raffaele Giancarlo, Filippo Utro · 2011 · Algorithms for Molecular Biology
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    Xiaoli Z. Fern, Wei Yen Lin · 2008 · Statistical Analysis and Data Mining The ASA Data Science Journal
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    Hai Yang, Rui Chen, Dongdong Li, Zhe Wang · 2021 · Bioinformatics
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    Hongfu Liu, Tongliang Liu, Junjie Wu, Dacheng Tao, Yun Fu · 2015 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
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    Yanxiao Zhang, Lada A. Koneva, Shama Virani, Anna E. Arthur, Alisha Virani, Pelle B. Hall, Charles Warden, Thomas E. Carey, Douglas Brian Chepeha, Mark E. Prince et al. · 2016 · Clinical Cancer Research
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    Christopher R. John, David Watson, Dominic Russ, Katriona Goldmann, Michael R. Ehrenstein, Costantino Pitzalis, Myles Lewis, Michael R. Barnes · 2020 · Scientific Reports
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    Tang, Chad, Hobbs, Brian, Amer, Ahmed, Li, Xiao, Behrens, Carmen, Canales, Jaime Rodriguez, Cuentas, Edwin Parra, Villalobos, Pamela, Fried, David, Chang, Joe Y. et al. · 2018 · Scientific Reports
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    J. Matthew Mahoney, Jaclyn Taroni, Viktor Martyanov, Tammara A. Wood, Casey S. Greene, Patricia Ann Pioli, Monique Hinchcliff, Michael L. Whitfield · 2015 · PLoS Computational Biology
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    Wenjuan Zhang, Mengjie Fang, Di Dong, Xiaoxiao Wang, Xiaoai Ke, Liwen Zhang, Chaoen Hu, Lingyun Guo, Xiaoying Guan, Junlin Zhou et al. · 2019 · Radiotherapy and Oncology
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    Alex Endert, Mahmud Shahriar Hossain, Naren Ramakrishnan, Chris L. North, Patrick Fiaux, Christopher Andrews · 2014 · Journal of Intelligent Information Systems
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    Tom Ronan, Zhijie Qi, Kristen M. Naegle · 2016 · Science Signaling
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    Lan Zhao, Victor Ho-Fun Lee, Michael K. Ng, Hong Yan, Maarten F. Bijlsma · 2018 · Briefings in Bioinformatics
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    Wenting Tu, Guangyuan Sun, Li Fan, Yun Wang, Yi Xia, Yu Guan, Qiong Li, Di Zhang, Shiyuan Liu, Zhaobin Li · 2019 · Lung Cancer
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    Kelly V. Ruggles, Karsten Krug, Xiaojing Wang, Karl R. Clauser, Jing Wang, Samuel Payne, David Fenyö, Bing Zhang, D.R. Mani · 2017 · Molecular & Cellular Proteomics
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    Yi Hong, Sam Kwong, Yuchou Chang, Qingsheng Ren · 2008 · Pattern Recognition
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    Stephen Swift, Allan Tucker, Veronica Vinciotti, Nigel Martin, Christine Anne Orengo, Xiaohui Liu, Paul Kellam · 2004 · Genome biology
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    Theresa Ullmann, Christian Martin Hennig, Anne‐Laure Boulesteix · 2021 · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
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    Yingwei Hu, Jianbo Pan, Punit Shah, Minghui Ao, Stefani N. Thomas, Yang Liu, Lijun Chen, Michael Schnaubelt, David J. Clark, Henry Rodriguez et al. · 2020 · Cell Reports
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    Chen Meng, Dominic Helm, Martin Frejno, Bernhard Küster · 2015 · Journal of Proteome Research
79. PINSPlus: a tool for tumor subtype discovery in integrated genomic data
    Hung Nguyen, Sangam Shrestha, Sorin Drăghici, Tin Nguyen · 2018 · Bioinformatics
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    Maisa Daoud, Michael Mayo · 2019 · Artificial Intelligence in Medicine
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    Ramazan Ünlü, Petros Xanthopoulos · 2019 · Expert Systems with Applications
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    Benjamin G. Barwick, Paola E. Neri, Nizar Jacques Bahlis, Ajay K. Nooka, Madhav V. Dhodapkar, David L. Jaye, Craig C. Hofmeister, Jonathan L. Kaufman, Vikas Anand Gupta, Daniel Auclair et al. · 2019 · Nature Communications
83. Nonnegative Matrix Factorization for Interactive Topic Modeling and Document Clustering
    Da Kuang, Jaegul Choo, Haesun Park · 2014 · Springer eBooks
84. Distinct transcriptional programs stratify ovarian cancer cell lines into the five major histological subtypes
    Bethany M. Barnes, Louisa Nelson, Anthony Tighe, George J. Burghel, I‐Hsuan Lin, Sudha N. Desai, Joanne C. McGrail, Robert David Morgan, Stephen S. Taylor · 2021 · Genome Medicine
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    Tahani Alqurashi, Wenjia Wang · 2018 · International Journal of Machine Learning and Cybernetics
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    Christian E. Lopez, Scott M. Tucker, Tarik J. Salameh, Conrad S. Tucker · 2018 · Journal of Biomedical Informatics
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    Loris De Cecco, Monica Nicolau, Marco Giannoccaro, Maria Grazia Daidone, Paolo Bossi, Laura D. Locati, Lisa Francesca Licitra, Silvana Canevari · 2015 · Oncotarget
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    Bum Chul Kwon, Benjamin Eysenbach, Janu Verma, Kenney Ng, Christopher R. de Filippi, Walter F. Stewart, Adam Perer · 2017 · IEEE Transactions on Visualization and Computer Graphics
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    Behrouz Minaei Bidgoli, Alexander P. Topchy, William F. Punch · 2004 · International Conference on Information Technology: Coding and Computing (ITCC)
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    Jeffrey Nirschl, Andrew R. Janowczyk, Eliot G. Peyster, Renee Frank, Kenneth B. Margulies, Michael D. Feldman, Anant Madabhushi · 2018 · PLoS ONE
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    Ana Carolina Lorena, André C. P. L. F. de Carvalho · 2008 · Neurocomputing
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    Furong Liu, Lu Qin, Zhibin Liao, Jia Song, Chaoyi Yuan, Yachong Liu, Yu Wang, Heze Xu, Qiaofeng Zhang, Youliang Pei et al. · 2020 · Experimental Hematology and Oncology
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    Joseph Elan Grossman, Lakshmi B. Muthuswamy, Ling Huang, Dipikaa Akshinthala, Sofia Perea, Raul S. González, Leo L. Tsai, Jonah Cohen, Bruno Bockorny, Andrea J. Bullock et al. · 2021 · Clinical Cancer Research
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    Junjie Wu · 2012 · Springer theses
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    Karama Asleh, Gian Luca Negri, Sandra E. Spencer Miko, Shane Colborne, Christopher S. Hughes, Xiu Q. Wang, Dongxia Gao, C. Blake Gilks, Stephen Chia, Torsten O. Nielsen et al. · 2022 · Nature Communications
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    Harun Pirim, Burak Ekşioğlu, Andy Perkins, Çetin Yüceer · 2012 · Computers & Operations Research
97. Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data
    Malik Yousef, Segun C. Jung, Louise C. Showe, Michael K. Showe · 2007 · BMC Bioinformatics
98. Lesional gene expression profiling in cutaneous T-cell lymphoma reveals natural clusters associated with disease outcome
    Jessica J. Shin, Stefano Monti, Daniel Aires, Madeleine A. Duvic, Todd R. Golub, David A. Jones, Thomas Seth Kupper · 2007 · Blood
99. ConsensusCluster: A Software Tool for Unsupervised Cluster Discovery in Numerical Data
    Michael Seiler, Changquan Huang, Sándor Szalma, Gyan Bhanot · 2010 · OMICS A Journal of Integrative Biology
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     Hengrui Liu, Angela Dong, Ayana Meegol Rasteh, Panpan Wang, Jieling Weng · 2024 · Scientific Reports