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Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data
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“Using the Consensus Clustering24 method (Supplementary Methods 9.1–9.2, Extended Data Fig. 9), we identified five major proteomic subtypes in this tumor cohort, with 15, 9, 25, 11, and 19 cases in subtypes A through E, respectively (Fig. 4a–b).”
From Proteogenomic characterization of human colon and rectal cancer (2014) · cites this paper as [24]
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“We previously identified three robust and reproducible transcriptomic sub-groups of KRAS-mutant LUAD by applying non-negative matrix factorization consensus clustering81–83 to RNASeq data from 68 tumors from The Cancer Genome Atlas (TCGA) dataset67.”
From Co-occurring genomic alterations in non-small-cell lung cancer biology and therapy (2019) · cites this paper as [83]
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An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Kohonen, T. (1995) · Springer series in information sciences · doi:10.1007/978-3-642-56927-2 · Cited by 16,032
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558
Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., et al. (1999) · Science · doi:10.1126/science.286.5439.531 · Cited by 11,516
Self-citationKohonen, T. (1990) · Proceedings of the IEEE · doi:10.1109/5.58325 · Cited by 8,414
Hubert, L., & Arabie, P. (1985) · Journal of Classification · doi:10.1007/bf01908075 · Cited by 8,219
Algorithms for Clustering Data
Sarle, W. S., Jain, A. K., & Dubes, R. C. (1990) · Technometrics · doi:10.2307/1268876 · Cited by 7,893
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- Cowell, F. A. (1995). Measuring Inequality. New York: Prentice Hall.
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Items 701–800 of 1,002
Thongprayoon, C., Kattah, A. G., Mao, M. A., Keddis, M. T., Pattharanitima, P., Vallabhajosyula, S., Nissaisorakarn, V., Erickson, S. B., Dillon, J. J., Garovic, V. D., et al. (2021) · QJM · doi:10.1093/qjmed/hcab194 · Cited by 23
Cohen, A. D., Chang, C., & Wang, Y. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118555 · Cited by 23
Live-cell fluorescence spectral imaging as a data science challenge
Acuña-Rodriguez, J. P., Mena-Vega, J. P., & Argüello‐Miranda, O. (2022) · Biophysical Reviews · doi:10.1007/s12551-022-00941-x · Cited by 24
Liu, Q., Yang, X., Yin, Y., Zhang, H., Yin, F., Guo, P., Zhang, X., Sun, C., Li, S., Han, Y., et al. (2022) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2022/6575534 · Cited by 23
Gene Selection Using Rough Set Theory
Li, D., & Zhang, W. (2006) · Lecture notes in computer science · doi:10.1007/11795131_113 · Cited by 23
Morphological signatures and genomic correlates in glioblastoma
Cooper, L., Kong, J., Wang, F., Kurç, T., Moreno, C. S., Brat, D. J., & Saltz, J. H. (2011) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2011.5872714 · Cited by 23
BioSig3D: High Content Screening of Three-Dimensional Cell Culture Models
Bilgin, C. C., Fontenay, G., Cheng, Q., Chang, H., Han, J., & Parvin, B. (2016) · PLoS ONE · doi:10.1371/journal.pone.0148379 · Cited by 23
Shrinkage Clustering: a fast and size-constrained clustering algorithm for biomedical applications
Hu, C. W., Li, H., & Qutub, A. A. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2022-8 · Cited by 25
Winham, S. J., Larson, N. B., Armasu, S. M., Fogarty, Z. C., Larson, M. C., McCauley, B. M., Wang, C., Lawrenson, K., Gayther, S. A., Cunningham, J. M., et al. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy444 · Cited by 23
Model-based clustering of censored data via mixtures of factor analyzers
Wang, W., Castro, L. M., Lachos, V. H., & Lin, T. (2019) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2019.06.001 · Cited by 23
Selecting Diversifying Heuristics for Cluster Ensembles
Hadjitodorov, S. T., & Kuncheva, L. I. (2007) · Springer eBooks · doi:10.1007/978-3-540-72523-7_21 · Cited by 23
Ensemble Clustering using Semidefinite Programming.
Singh, V., Mukherjee, L., Peng, J., & Xu, J. (2007) · PubMed · Cited by 23
Versi, A., Azim, A., Ivan, F. X., Abdel‐Aziz, M. I., Bates, S., Riley, J., Uddin, M., Kermani, N. Z., der Zee, A. H. M., Dahlen, S., et al. (2024) · Clinical and Translational Medicine · doi:10.1002/ctm2.70007 · Cited by 22
Unifying Gene Expression Measures from Multiple Platforms Using Factor Analysis
Wang, X. V., Verhaak, R. G. W., Purdom, E. A., Spellman, P. T., & Speed, T. P. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017691 · Cited by 22
NBIA: a network-based integrative analysis framework – applied to pathway analysis
Nguyen, T., Shafi, A., Nguyen, T.-M., Schissler, A. G., & Drăghici, S. (2020) · Scientific Reports · doi:10.1038/s41598-020-60981-9 · Cited by 22
Immune-Related Gene-Based Novel Subtypes to Establish a Model Predicting the Risk of Prostate Cancer
Zhang, E., He, J., Zhang, H., Shan, L., Wu, H., Zhang, M., & Song, Y. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.595657 · Cited by 22
Miculan, M., Nelissen, H., Hassen, M. B., Marroni, F., Inzé, D., Pè, M. E., & Dell’Acqua, M. (2021) · The Plant Journal · doi:10.1111/tpj.15364 · Cited by 23
Dynamic functional hippocampal markers of residual depressive symptoms in euthymic bipolar disorder
Saccaro, L. F., Gaviria, J., Ville, D. V. D., & Piguet, C. (2023) · Brain and Behavior · doi:10.1002/brb3.3010 · Cited by 22
Liu, C., Belleau, E. L., Dong, D., Sun, X., Xiong, G., Pizzagalli, D. A., Auerbach, R. P., Wang, X., & Yao, S. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.05.074 · Cited by 22
Network medicine for patients' stratification: From single‐layer to multi‐omics
Petti, M., & Farina, L. (2023) · WIREs Mechanisms of Disease · doi:10.1002/wsbm.1623 · Cited by 23
Zhou, J., Li, M., Li, Y., Xiao, Y., Luo, X., Gao, S., Zhi-Min, M. H.-P. A. L., Sadowski, N., Timp, W., Dardick, C. D., et al. (2023) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiad409 · Cited by 22
Pan, B., Ma, X., Zhou, S., Cheng, X., Fang, J., Yi, Q., Li, Y., Li, S., & Yang, J. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1370647 · Cited by 22
PSO driven collaborative clustering: A clustering algorithm for ubiquitous environments
Depaire, B., Falcón, R., Vanhoof, K., & Wets, G. (2011) · Intelligent Data Analysis · doi:10.3233/ida-2010-0455 · Cited by 22
Lee, S., Song, K. H., Koo, I., Lee, K.-H., Suh, K., & Kim, B. (2012) · Genomics · doi:10.1016/j.ygeno.2012.04.004 · Cited by 22
Sung, C. O., Song, I.-H., & Sohn, I. (2013) · Gynecologic Oncology · doi:10.1016/j.ygyno.2013.11.036 · Cited by 22
Partial correlation matrix estimation using ridge penalty followed by thresholding and re‐estimation
Ha, M. J., & Sun, W. (2014) · Biometrics · doi:10.1111/biom.12186 · Cited by 22
Ren, Z., Wang, W., & Li, J. (2015) · International Journal of Oncology · doi:10.3892/ijo.2015.3263 · Cited by 23
Wen, Y., Wei, Y., Zhang, S., Li, S., Liu, H., Wang, F., Zhao, Y., Zhang, D., & Zhang, Y. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw028 · Cited by 22
Chen, F., Zhao, Z., Gao, C., Liu, J., Su, X., Zhao, J., Tang, P., & Liao, H. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2761980 · Cited by 22
Interactive visual exploration and refinement of cluster assignments
Kern, M., Lex, A., Gehlenborg, N., & Johnson, C. R. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1813-7 · Cited by 22
Iam-On, N. (2019) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-019-00989-4 · Cited by 22
Defining subpopulations of differential drug response to reveal novel target populations
Keshava, N., Toh, T. S., Yuan, H., Yang, B., Menden, M. P., & Wang, D. Y. Q. (2019) · npj Systems Biology and Applications · doi:10.1038/s41540-019-0113-4 · Cited by 22
Deng, J., Zeng, W., Kong, W., Shi, Y., & Mou, X. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.01003 · Cited by 23
Microstructure Classification in the Unsupervised Context
Kunselman, C., Sheikh, S., Mikkelsen, M., Attari, V., & Arróyave, R. (2020) · Acta Materialia · doi:10.1016/j.actamat.2021.117434 · Cited by 22
EXPLORING BIOLOGICAL NETWORK DYNAMICS WITH ENSEMBLES OF GRAPH PARTITIONS
Navlakha, S., & Kingsford, C. (2009) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789814295291_0019 · Cited by 22
Learning multiple nonredundant clusterings
Cui, Y. D., Fern, X. Z., & Dy, J. (2010) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1839490.1839496 · Cited by 22
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Bhargavi, M. S., & Gowda, S. D. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.04.023 · Cited by 22
A new validity measure for a correlation-based fuzzy c-means clustering algorithm
Zhang, M., Zhang, W., Sicotte, H., & Yang, P. (2009) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2009.5332582 · Cited by 22
He, Z., Zhang, J., Yuan, X., Liu, Z., Liu, B., Tuo, S., & Liu, Y. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177662 · Cited by 23
Li, Y., Wang, B., Ma, F., Jiang, D., Wang, Y., Li, K., Tan, S., Feng, J., Wang, Y., Qin, Z., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101311 · Cited by 23
Bodinier, M., Peronnet, E., Llitjos, J., Kreitmann, L., Brengel‐Pesce, K., Rimmele, T. R., Fleurie, A., Textoris, J., Venet, F., Maucort‐Boulch, D., et al. (2024) · Critical Care · doi:10.1186/s13054-024-04990-4 · Cited by 21
A review on multi-omics integration for aiding study design of large scale TCGA cancer datasets
Han, E., Kwon, H., & Jung, I. (2025) · BMC Genomics · doi:10.1186/s12864-025-11925-y · Cited by 22
Xiong, T., Wang, M., Zhao, J., Liu, Q., Yang, C., Luo, W., Li, X., Yang, H., Kristiansen, K., Roy, B., et al. (2017) · Oncotarget · doi:10.18632/oncotarget.17989 · Cited by 21
A ternary bitwise calculator based genetic algorithm for improving error correcting output codes
Ye, X.-N., Liu, K., & Liong, S. (2020) · Information Sciences · doi:10.1016/j.ins.2020.05.088 · Cited by 21
Differential response to scrambler therapy by neuropathic pain phenotypes
Min, Y. G., Baek, H. S., Lee, K., & Hong, Y. (2021) · Scientific Reports · doi:10.1038/s41598-021-89667-6 · Cited by 21
RobustECD: Enhancement of Network Structure for Robust Community Detection
Zhou, J., Chen, Z., Du, M., Chen, L., Yu, S., Chen, G., & Xuan, Q. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3088844 · Cited by 21
Singh, A., Horng, H., Roshkovan, L., Weeks, J. K., Hershman, M., Noël, P. B., Luna, J. M., Cohen, E. A., Pantalone, L., Shinohara, R. T., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-14160-7 · Cited by 21
Patiño, C. P. B., Soares, J. M., Blanco, K. C., Inada, N. M., & Bagnato, V. S. (2023) · Antibiotics · doi:10.3390/antibiotics12101502 · Cited by 21
Drago, C., Nallo, L. D., & Russotto, M. L. (2024) · Environmental Impact Assessment Review · doi:10.1016/j.eiar.2024.107434 · Cited by 21
Rough subspace-based clustering ensemble for categorical data
Gao, C., Witold Pedrycz, C., & Miao, D. (2013) · Soft Computing · doi:10.1007/s00500-012-0972-8 · Cited by 21
Devarajan, K., Wang, G., & Ebrahimi, N. B. (2014) · Machine Learning · doi:10.1007/s10994-014-5470-z · Cited by 21
Chon, T., Qu, X., Cho, W.-S., Hwang, H.-J., Tang, H., Liu, Y., Choi, J.-H., Jung, M. H., Chung, B. S., Lee, H. Y., et al. (2013) · Ecological Informatics · doi:10.1016/j.ecoinf.2013.06.004 · Cited by 21
Liu, G., Chen, Z., Danilova, I. G., Bolkov, M. A., Тузанкина, И. А., & Liu, G. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00422 · Cited by 21
Ming, W., Xie, H., Hu, Z., Chen, Y., Zhu, Y., Bai, Y., Liu, H., Sun, X. S., Liu, Y., & Gu, W. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.00985 · Cited by 24
Chen, J., Milot, L. M., Cheung, H., & Martel, A. L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32251-9_63 · Cited by 21
Search strategies in a human water maze analogue analyzed with automatic classification methods
Schoenfeld, R., Moenich, N., Mueller, F.-J., Lehmann, W., & Leplow, B. (2009) · Behavioural Brain Research · doi:10.1016/j.bbr.2009.11.022 · Cited by 21
Scatter/Gather Clustering: Flexibly Incorporating User Feedback to Steer Clustering Results
Hossain, M. S., Ojili, P. K. R., Grimm, C. M., Müller, R., Watson, L. T., & Ramakrishnan, N. (2012) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.258 · Cited by 21
A New Efficient Approach in Clustering Ensembles
Azimi, J., Abdoos, M., & Analoui, M. (2007) · Springer eBooks · doi:10.1007/978-3-540-77226-2_41 · Cited by 21
Alexe, G., Dalgin, G. S., Ramaswamy, R., DeLisi, C., & Bhanot, G. (2006) · Cancer Informatics · doi:10.1177/117693510600200006 · Cited by 21
Identification of Endotypes of Hospitalized COVID-19 Patients
Ranard, B. L., Megjhani, M., Terilli, K., Doyle, K. W., Claassen, J., Pinsky, M. R., Clermont, G., Yoram Vodovotz, B., Asgari, S., & Park, S. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.770343 · Cited by 20
Deep structure integrative representation of multi-omics data for cancer subtyping
Yang, B., Yang, Y. L., & Su, X. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac345 · Cited by 20
The proteome of clear cell ovarian carcinoma
Ji, J. X. Y., Cochrane, D. R., Negri, G. L., Colborne, S., Miko, S. E. S., Hoang, L. N., Farnell, D., Tessier‐Cloutier, B., Huvila, J. K., Thompson, E. F., et al. (2022) · The Journal of Pathology · doi:10.1002/path.6006 · Cited by 20
Multivalent state transitions shape the intratumoral composition of small cell lung carcinoma
Gopal, P., Petty, A. P., Rogacki, K. R., Bera, T. K., Bareja, R., Peacock, C. D., & Abazeed, M. E. (2022) · Science Advances · doi:10.1126/sciadv.abp8674 · Cited by 20
Zhong, M., Duan, X., Nijiati, M., Qi, H., Xu, D., Cai, D., Li, C., Huang, Z.-P., Zhu, Q., Gao, F., et al. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03788-8 · Cited by 20
Exploring the latent structure of behavior using the Human Connectome Project’s data
Schöttner, M., Bolton, T. A. W., Patel, J. H., Nahálka, A. T., Vieira, S., & Hagmann, P. (2023) · Scientific Reports · doi:10.1038/s41598-022-27101-1 · Cited by 20
A time-resolved meta-analysis of consensus gene expression profiles during human T-cell activation
Rade, M., Böhlen, S., Neuhaus, V., Löffler, D., Blumert, C., Merz, M., Koehl, U., Dehmel, S., Sewald, K., & Reiche, K. (2023) · Genome biology · doi:10.1186/s13059-023-03120-7 · Cited by 24
DeCoClu: Density consensus clustering approach for public transport data
Fiori, A., Mignone, A., & Rospo, G. (2015) · Information Sciences · doi:10.1016/j.ins.2015.08.054 · Cited by 20
Wang, Z., Zhang, C., Cai, J., Li, Q., Wang, Z., & Jiang, T. (2015) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-015-0249-z · Cited by 20
Liu, Q., Zhao, S., Su, P.-F., & Shyr, Y. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-s5-s7 · Cited by 20
A graph clustering algorithm based on a clustering coefficient for weighted graphs
Nascimento, M. C. V., & de Carvalho, A. C. P. L. F. (2010) · Journal of the Brazilian Computer Society · doi:10.1007/s13173-010-0027-x · Cited by 20
Similar Squamous Cell Carcinoma Epithelium microRNA Expression in Never Smokers and Ever Smokers
Kolokythas, A., Zhou, Y., SCHWARTZ, J. L., & Adami, G. R. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141695 · Cited by 20
Hossen, M. B. (2015) · Computational Biology and Bioinformatics · doi:10.11648/j.cbb.20150306.12 · Cited by 20
Consensus-Based Sorting of Neuronal Spike Waveforms
Fournier, J., Mueller, C. M., Idelson, M. S., Hemberger, M., & Laurent, G. (2016) · PLoS ONE · doi:10.1371/journal.pone.0160494 · Cited by 20
Lu, X., Zhang, Q., Wang, Y., Zhang, L., Zhao, H., Chen, C., Wang, Y., Liu, S., Lu, T., Wang, F., et al. (2018) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-018-2684-7 · Cited by 20
Cluster Analysis of Microarray Data
Franco, M., & Vivo, J.-M. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9442-7_7 · Cited by 21
Leung, R., Balamurali, M., & Melkumyan, A. (2019) · Mathematical Geosciences · doi:10.1007/s11004-019-09839-z · Cited by 20
Greenbury, S. F., Ougham, K., Wu, J., Battersby, C., Gale, C., Modi, N., & Angelini, E. D. (2021) · Scientific Reports · doi:10.1038/s41598-021-85878-z · Cited by 20
Analysing surnames as geographic data.
Cheshire, J. A. (2014) · PubMed · doi:10.4436/jass.92004 · Cited by 20
Ensemble Clustering with a Fuzzy Approach
Avogadri, R., & Valentini, G. (2008) · Studies in computational intelligence · doi:10.1007/978-3-540-78981-9_3 · Cited by 20
Mahrooghy, M., Younan, N. H., Anantharaj, V. G., Aanstoos, J. V., & Yarahmadian, S. (2012) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2012.2201449 · Cited by 20
Clements, M., van Someren, E. P., Knijnenburg, T., & Reinders, M. J. T. (2007) · Genomics Proteomics & Bioinformatics · doi:10.1016/s1672-0229(07)60019-9 · Cited by 20
Clustering high throughput biological data with B-MST, a minimum spanning tree based heuristic
Pirim, H., Ekşioğlu, B., & Perkins, A. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.03.031 · Cited by 19
Hybrid Density- and Partition-Based Clustering Algorithm for Data With Mixed-Type Variables
Wang, S., Yabes, J. G., & Chang, C.-C. H. (2021) · Journal of Data Science · doi:10.6339/21-jds996 · Cited by 19
Tanaka, N., Mori, S., Kiyotani, K., Ota, Y., Gotoh, O., Kusumoto, S., Nakano, N., Suehiro, Y., Ito, A., Choi, I., et al. (2022) · Haematologica · doi:10.3324/haematol.2021.280352 · Cited by 19
Gross, R. S., Thaweethai, T., Rosenzweig, E. B., Chan, J. G. M., Chibnik, L. B., Cicek, M. S., Elliott, A., Flaherman, V. J., Foulkes, A. S., Witvliet, M. G., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0285635 · Cited by 19
Giancarlo, R., & Utro, F. (2012) · Theoretical Computer Science · doi:10.1016/j.tcs.2012.01.024 · Cited by 19
Lee, C. M., Mudaliar, M., Haggart, D. R., Wolf, C. R., Miele, G., Vass, J. K., Higham, D. J., & Crowther, D. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0048238 · Cited by 19
New resampling method for evaluating stability of clusters
Dresen, I. G., Boes, T., Huesing, J., Neuhäeuser, M., & Joeckel, K.-H. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-42 · Cited by 19
Tradict enables accurate prediction of eukaryotic transcriptional states from 100 marker genes
Biswas, S., Kerner, K., Teixeira, P. J. P. L., Dangl, J. L., Jojic, V., & Wigge, P. A. (2017) · Nature Communications · doi:10.1038/ncomms15309 · Cited by 19
Trilla‐Fuertes, L., Gámez‐Pozo, A., Prado-Vázquez, G., Zapater‐Moros, A., Díaz‐Almirón, M., Arevalillo, J. M., Ferrer-Gómez, M., Navarro, H., Maı́n, P., Espinosa, E., et al. (2019) · BMC Cancer · doi:10.1186/s12885-019-5858-z · Cited by 19
Wagner, F., Ott, T., Schall, M., Lautenschlager, U., Vogt, R., & Oberprieler, C. (2019) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2019.106702 · Cited by 19
Enhancement of decomposed spectral coherence using sparse nonnegative matrix factorization
Lee, J.-H. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.107747 · Cited by 19
Byron, S. A., Hendricks, W. P. D., Nagulapally, A. B., Kraveka, J. M., Ferguson, W. S., Brown, V. I., Eslin, D. E., Mitchell, D., Cornelius, A., Roberts, W. D., et al. (2021) · Cancer Research · doi:10.1158/0008-5472.can-21-1033 · Cited by 20
Xu, P., Zhang, C., Wang, J., Wang, P., Gao, Y., Shang, S., Guo, S., Li, X., Zhi, H., & Ning, S. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab481 · Cited by 19
MODEC: an unsupervised clustering method integrating omics data for identifying cancer subtypes
Zhang, Y., & Kiryu, H. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac372 · Cited by 19
Correlation Clustering for Learning Mixtures of Canonical Correlation Models
Fern, X. Z., Brodley, C. E., & Friedl, M. A. (2005) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972757.39 · Cited by 19
Sessler, N., Krug, K., Nordheim, A., Mordmüller, B. G., & Maček, B. (2012) · Amino Acids · doi:10.1007/s00726-012-1296-9 · Cited by 19
Moarii, M., Reyal, F., & Vert, J. (2015) · Human Genomics · doi:10.1186/s40246-015-0048-9 · Cited by 19
Evaluation measures for cluster ensembles based on a fuzzy generalized Rand index
Yeh, C.-C., & Yang, M. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.03.030 · Cited by 19
Giurcăneanu, C. D., & Tăbuş, I. (2004) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/s1110865704309078 · Cited by 19
Metadata
- Authors
- Monti, S., Tamayo, P., Mesirov, J., & Golub, T.
- Affiliations
- Massachusetts Institute of Technology (US)
- Venue
- Machine Learning
- Published
- 2003-07
- Volume
- 52
- Issue
- 1-2
- Pages
- 91-118
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1548779692
- Cited by
- 2,116
- References
- 49
- Citations by year
- 2026: 134 · 2025: 113 · 2024: 155 · 2023: 133 · 2022: 139 · 2021: 192 · 2020: 123 · 2019: 112 · 2018: 120 · 2017: 100 · 2016: 85 · 2015: 94 · 2014: 78 · 2013: 77 · 2012: 80
- FWCI
- 8.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Keywords
- consensus clustering, gene expression microarrays, cluster validation, cluster stability, resampling, cluster visualization, optimal number of clusters, K-means clustering, self-organizing maps, Bayesian clustering, sensitivity to initial conditions
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