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Clustering of high throughput gene expression data. (English) Zbl 1349.62554
Summary: High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Bioinformatics, computational biology, and systems biology deal with biological problems using computational methods. Clustering is one of the methods used to gain insight into biological processes, particularly at the genomics level. Clearly, clustering can be used in many areas of biological data analysis. However, this paper presents a review of the current clustering algorithms designed especially for analyzing gene expression data. It is also intended to introduce one of the main problems in bioinformatics – clustering gene expression data – to the operations research community.

62P10 Applications of statistics to biology and medical sciences; meta analysis
62H30 Classification and discrimination; cluster analysis (statistical aspects)
92D10 Genetics and epigenetics
Full Text: DOI
[1] Adamcsek, B.; Palla, G.; Farkas, I.J.; Derényi, I.; Vicsek, T., Cfinder: locating cliques and overlapping modules in biological networks, Bioinformatics, 22, 8, 1021-1023, (2006)
[2] Agarwal, G.; Kempe, D., Modularity-maximizing graph communities via mathematical programming, European physical journal B, 66, 409-418, (2008) · Zbl 1188.90262
[3] Alderson, D.L., Catching the network science bug: insight and opportunity for the operations researcher, Operations research, 56, 5, 1047-1065, (2008) · Zbl 1167.90390
[4] Allison, D.B.; Page, G.P.; Beasley, T.M.; Edwards, J.W., DNA microarrays and related genomics techniques: design, analysis, and interpretation of experiments (biostatistics), (2005), Chapman and Hall/CRC
[5] Alshalalfah, M.; Alhajj, R., Cancer class prediction: two stage clustering approach to identify informative genes, Intelligent data analysis, 13, 4, 671-686, (2009)
[6] Andreopoulos, B.; An, A.; Wang, X.; Schroeder, M., A roadmap of clustering algorithms: finding a match for a biomedical application, Briefings in bioinformatics, 10, 3, 297-314, (2009)
[7] Androulakis IP. Mathematical programming approaches for the analysis of microarray data. In: Handbook of optimization in medicine, vol. 26. Springer; 2009. p. 357-78.
[8] Asyali, M.H.; Colak, D.; Demirkaya, O.; Inan, M.S., Gene expression profile classification: a review, Current bioinformatics, 1, 55-73, (2006)
[9] Balasubramaniyan, R.; Hüllermeier, E.; Weskamp, N.; Kämper, J., Clustering of gene expression data using a local shape-based similarity measure, Bioinformatics, 21, 7, 1069-1077, (2005)
[10] Bandyopadhyay, S.; Bhattacharyya, M., Analyzing mirna co-expression networks to explore TF-mirna regulation, BMC bioinformatics, 10, 163, 1-16, (2009)
[11] Bandyopadhyay, S.; Mukhopadhyay, A.; Maulik, U., An improved algorithm for clustering gene expression data, Bioinformatics, 23, 21, 2859-2865, (2007)
[12] Bandyopadhyay, S.; Pal, S.K., Dynamic range-based distance measure for microarray expressions and a fast gene-ordering algorithm, IEEE transactions on systems, man, and cybernetics, 37, 3, 742-749, (2007)
[13] Barthelemy, P.; Brucher, F.; Osswald, C., Combinatorial optimisation and hierarchical classifications, Annals of operations research, 153, 1, 179-214, (2007) · Zbl 1157.90501
[14] Berretta Regina, Mendes Alexandre, Moscato Pablo. Integer programming models and algorithms for molecular classification of cancer from microarray data. In: Estivill-Castro, editor. Proceedings of the Twenty-eighth Australasian conference on computer science, vols. 38, 27; 2005. p. 361-70
[15] Beyer A. Network-based models in molecular biology. In: Ganguly Niloy, Deutsch Andreas, Mukherjee Animesh, Bellomo Nicola, editors. Dynamics on and of complex networks; 2009. p. 35-56.
[16] Bohland, J.W.; Bokil, H.; Pathak, S.D.; Lee, C.K.; Ng, L.; Lau, C., Clustering of spatial gene expression patterns in the mouse brain and comparison with classical neuroanatomy, Methods, 50, 2, 105-112, (2010)
[17] Boscolo, R.; Sabatti, C.; Liao, J.C.; Roychowdhury, V.P., A generalized framework for network component analysis, IEEE/ACM transactions on computational biology and bioinformatics, 2, 4, 289-301, (2005)
[18] Brandes, U.; Delling, D.; Gaertler, M.; Gorke, R.; Hoefer, M.; Nikoloski, Z., On modularity clustering, IEEE transactions on knowledge and data engineering, 20, 2, 172-188, (2008)
[19] Bushel, P.R., Clustering of gene expression data and end-point measurements by simulated annealing, Journal of bioinformatics and computational biology, 7, 1, 193-215, (2009)
[20] Cano, C.; Garcia, F.; Lopez, F.J.; Blanco, A., Intelligent system for the analysis of microarray data using principal components and estimation of distribution algorithms, Expert systems with applications, 36, 3, 4654-4663, (2009)
[21] Ceccarelli, M.; Maratea, A., Improving fuzzy clustering of biological data by metric learning with side information, International journal of approximate reasoning, 47, 1, 45-57, (2008) · Zbl 1189.92001
[22] Chen, J.; Hsu, W.; Lee, M.L.; Ng, S.K., Discovering reliable protein interactions from high-throughput experimental data using network topology, Artificial intelligence in medicine, 35, 37-47, (2005)
[23] Chen, W.Y.C.; Dress, A.W.M.; Yu, W.Q., Community structure of networks, Mathematics in computer science, 1, 3, 441-457, (2008) · Zbl 1203.90167
[24] Chipman, H.; Tibshirani, R., Hybrid hierarchical clustering with applications to microarray data, Biostatistics, 7, 2, 286-301, (2006) · Zbl 1169.62368
[25] Chouakria, A.D.; Diallo, A.; Giroud, F., Adaptive clustering for time series: application for identifying cell cycle expressed genes, Computational statistics and data analysis, 53, 4, 1414-1426, (2009) · Zbl 1452.62629
[26] Christinat, Y.; Wachmann, B.; Zhang, L., Gene expression data analysis using a novel approach to biclustering combining discrete and continuous data, IEEE/ACM transactions on computational biology and bioinformatics, 5, 4, 583-593, (2008)
[27] Clauset, A.; Moore, C.; Newman, M.E.J., Hierarchical structure and the prediction of missing links in networks, Nature, 453, 7191, 98-101, (2008)
[28] Clauset, A.; Newman, M.E.J.; Moore, C., Finding community structure in very large networks, Physical review E, 70, (2004)
[29] Cohen, D.D.; Kasif, S.; Melkman, A.A., Seeing the forest for the trees: using the gene ontology to restructure hierarchical clustering, Bioinformatics, 25, 14, 1789-1795, (2009)
[30] Csardi, G.; Nepusz, T., The igraph software package for complex network research, Interjournal complex systems, 1695, (2006)
[31] Dharan, S.; Nair, A.S., Biclustering of gene expression data using reactive greedy randomized adaptive search procedure, BMC bioinformatics, 10, S27, 1-10, (2009)
[32] Dittrich, M.T.; Klau, G.W.; Rosenwald, A.; Dandekar, T.; Müller, T., Identifying functional modules in protein-protein interaction networks: an integrated exact approach, Bioinformatics, 24, 13, 223-231, (2008)
[33] Du, Z.; Wang, Y.; Ji, Z., Pk-means: a new algorithm for gene clustering, Computational biology and chemistry, 32, 4, 243-247, (2008) · Zbl 1159.92018
[34] Eckman, B.A.; Brown, P.G., Graph data management for molecular and cell biology, IBM journal of research and development, 50, 6, 545-560, (2006)
[35] Ernst, J.; Nau, G.J.; Joseph, Z.B., Clustering short time series gene expression data, Bioinformatics, 21, 1, 159-168, (2005)
[36] Faceli, K.; Souto, M.C.P.D.; Araujo, D.S.A.D.; Carvalhoüç, A.C.P.L.F.D., Multi-objective clustering ensemble for gene expression data analysis, Neurocomputing, 72, 2763-2774, (2009)
[37] Famili, A.F.; Liu, G.; Liu, Z., Evaluation and optimization of clustering in gene expression data analysis, Bioinformatics, 20, 10, 1535-1545, (2004)
[38] Fathian, M.; Amiri, B.; Maroosi, A., Application of honey-bee mating optimization algorithm on clustering, Applied mathematics and computation, 190, 2, 1502-1513, (2007) · Zbl 1117.92059
[39] Fortunato, S., Community detection in graphs, Physics reports, 486, 75-174, (2010)
[40] Fujita, A.; Sato, J.R.; Demasi, M.A.A.; Sogayar, M.C., Comparing Pearson, spearman and Hoeffding’s D measure for gene expression association analysis, Journal of bioinformatics and computational biology, 7, 4, 663-684, (2009)
[41] Garge, N.R.; Page, G.P.; Sprague, A.P.; Gorman, B.S.; Allison, D.B., Reproducible clusters from microarray research: whither?, BMC bioinformatics, 6, S10, 1-11, (2005)
[42] Geraci, F.; Leoncini, M.; Montangero, M.; Pellegrini, M.; Renda, M.E., K-boost: a scalable algorithm for high-quality clustering of microarray gene expression data, Journal of computational biology, 16, 6, 859-873, (2009)
[43] Ghouila, A.; Yahia, S.B.; Malouche, D.; Jmel, H.; Laouini, D.; Guerfali, F.Z., Application of multi-SOM clustering approach to macrophage gene expression analysis, Infection, genetics and evolution, 9, 3, 328-336, (2009)
[44] Girvan, M.; Newman, M.E.J., Community structure in social and biological networks, Proceedings of the national Academy of sciences of the united states of America, 99, 12, 7821-7826, (2002) · Zbl 1032.91716
[45] Glover, F.W.; Kochenberger, G., New optimization models for data mining, International journal of information technology and decision making, 5, 4, 605-609, (2006)
[46] Gómez, S.; Jensen, P.; Arenas, A., Analysis of community structure in networks of correlated data, Physical review E, 80, 016114, 1-5, (2009)
[47] Gungor, Z.; Unler, A., K-harmonic means data clustering with simulated annealing heuristic, Applied mathematics and computation, 184, 2, 199-209, (2007) · Zbl 1114.65009
[48] Gungor, Z.; Unler, A., K-harmonic means data clustering with tabu-search method, Applied mathematical modelling, 32, 6, 1115-1125, (2008) · Zbl 1145.68395
[49] Hagberg AA, Schult DA, Swart PJ. Exploring network structure, dynamics, and function using NetworkX. In: Proceedings of the seventh python in science conference (SciPy2008), Pasadena, CA USA; August 2008. p. 11-5.
[50] Hageman, J.A.; Berg, R.A.V.D.; Westerhuis, J.A.; Werf, M.J.V.D.; Smilde, A.K., Genetic algorithm based two-mode clustering of metabolomics data, Metabolomics, 4, 2, 141-149, (2008)
[51] Hayashida M, Sun F, Aburatani S, Horimoto K, Akutsu T. Integer programming-based approach to allocation of reporter genes for cell array analysis. In: The first international symposium on optimization and systems biology (OSB07); 2007. p. 288-301.
[52] He, Y.; Hui, S.C., Exploring ant-based algorithms for gene expression data analysis, Artificial intelligence in medicine, 47, 2, 105-119, (2009)
[53] Heath, J.W.; Fu, M.C.; Jank, W., New global optimization algorithms for model-based clustering, Computational statistics and data analysis, 53, 12, 3999-4017, (2009) · Zbl 1453.62108
[54] Higham, D.J.; Kalna, G., Spectral analysis of two-signed microarray gene expression data, Mathematical medicine and biology, 24, 2, 131-148, (2007) · Zbl 1148.92016
[55] Higham, D.J.; Kalna, G.; Kibble, M., Spectral clustering and its use in bioinformatics, Journal of computational and applied mathematics, 204, 25-37, (2007) · Zbl 1123.65024
[56] Hilt, S.W.; Yelundur, A.; McChesney, C.; Landry, M., Support vector machine implementations for classification and clustering, BMC bioinformatics, 7, 4, 1-18, (2006)
[57] Horst E. Distance measures for MPEG-7-based retrieval. In: MIR ’03: proceedings of the fifth ACM SIGMM international workshop on multimedia information retrieval. New York, NY, USA: ACM; 2003. p. 130-7.
[58] Hu, X.; Ng, M.; Wu, F.X.; Sokhansanj, B.A., Mining modeling, and evaluation of subnetworks from large biomolecular networks and its comparison study, IEEE transactions on information technology in biomedicine, 13, 2, 184-194, (2009)
[59] Hu, X.; Park, E.K.; Zhang, X., Microarray gene cluster identification and annotation through cluster ensemble and EM-based informative textual summarization, IEEE transactions on information technology in biomedicine, 13, 5, 832-840, (2009)
[60] Huang, D.; Pan, W., Incorporating biological knowledge into distance-based clustering analysis of microarray gene expression data, Bioinformatics, 22, 10, 1259-1268, (2006)
[61] Hubert, L.; Arabie, P., Comparing partitions, Journal of classification, 2, 193-218, (1985)
[62] Huttenhower, C.; Flamholz, A.I.; Landis, J.N.; Sahi, S.; Myers, C.L.; Olszewski, K.L., Nearest neighbor networks: clustering expression data based on gene neighborhoods, BMC bioinformatics, 8, 250, 1-13, (2007)
[63] Iyer, V.R.; Eisen, M.B.; Ross, D.T.; Schuler, G.; Moore, T.; Lee, J.C., The transcriptional program in the response of human fibroblasts to serum, Science, 283, 5398, 83-87, (1999)
[64] Jarboui, B.; Cheikh, M.; Siarry, P.; Rebai, A., Combinatorial particle swarm optimization (CPSO) for partitional clustering problem, Applied mathematics and computation, 192, 2, 337-345, (2007) · Zbl 1193.90219
[65] Jiang, D.; Tang, C.; Zhang, A., Cluster analysis for gene expression data: a survey, IEEE transactions on knowledge and data engineering, 16, 11, 1370-1386, (2004)
[66] Jonnalagadda, S.; Srinivasan, R., NIFTI: an evolutionary approach for finding number of clusters in microarray data, BMC bioinformatics, 10, 40, 1-13, (2009)
[67] Joshi, A.; Smet, R.D.; Marchal, K.; Peer, Y.V.D.; Michoel, T., Module networks revisited: computational assessment and prioritization of model predictions, Bioinformatics, 25, 4, 490-496, (2009)
[68] Kanehisa, M.; Bork, P., Bioinformatics in the post-sequence era, Nature genetics, 33, 305-310, (2003)
[69] Kaufman, L.; Rousseeuw, P., Finding groups in data: an introduction to cluster analysis, (1990), Wiley and Sons · Zbl 1345.62009
[70] Kelley, L.A.; Gardner, S.P.; Sutcliffe, M.J., An automated approach for clustering an ensemble of NMR-derived protein structures into conformationally related subfamilies, Protein engineering, 9, 11, 1063-1065, (1996)
[71] Kim, J.; Choi, S., Semidefinite spectral clustering, Pattern recognition, 39, 2025-2035, (2006) · Zbl 1102.68626
[72] Korkmaz, E.E.; Du, J.; Alhajj, R.; Barker, K., Combining advantages of new chromosome representation scheme and multi-objective genetic algorithms for better clustering, Intelligent data analysis, 10, 2, 163-182, (2006)
[73] Langfelder, P.; Zhang, B.; Horvath, S., Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for R, Bioinformatics applications note, 24, 5, 719-720, (2008)
[74] Lau, J.W.; Green, P.J., Bayesian model-based clustering procedures, Journal of computational and graphical statistic, 16, 3, 526-558, (2007)
[75] Lee, C.H.; Zaiane, O.R.; Park, H.H.; Huang, J.; Greiner, R., Clustering high dimensional data: a graph-based relaxed optimization approach, Information sciences, 178, 23, 4501-4511, (2008)
[76] Lee, W.P.; Tzou, W.S., Computational methods for discovering gene networks from expression data, Briefings in bioinformatics, 10, 4, 408-423, (2009)
[77] Li, G.; Ma, Q.; Tang, H.; Paterson, A.H.; Xu, Y., Qubic: a qualitative biclustering algorithm for analyses of gene expression data, Nucleic acids research, 37, 15, 1-10, (2009)
[78] Li, J.; Halgamuge, S.K.; Tang, S.L., Genome classification by gene distribution: an overlapping subspace clustering approach, BMC evolutionary biology, 8, 116, 1-15, (2008)
[79] Liang, F.; Wang, N., Dynamic agglomerative clustering of gene expression profiles, Pattern recognition letters, 28, 9, 1062-1076, (2007)
[80] Liu, J.; Li, Z.; Hu, X.; Chen, Y., Biclustering of microarray data with MOSPO based on crowding distance, BMC bioinformatics, 10, S9, 1-10, (2009)
[81] Liu, T.; Lin, N.; Shi, N.; Zhang, B., Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments, BMC bioinformatics, 10, 146, 1-20, (2009)
[82] Lucchetti, R.; Moretti, S.; Patrone, F.; Radrizzani, P., The Shapley and Banzhaf values in microarray games, Computers and operations research, 2342, (2009)
[83] Ma, P.C.H.; Chan, K.C.C., An iterative data mining approach for mining overlapping co-expression patterns in noisy gene expression data, IEEE transactions on nanobioscience, 8, 3, 252-258, (2009)
[84] Ma, P.C.H.; Chan, K.C.C., A novel approach for discovering overlapping clusters in gene expression data, IEEE transactions on biomedical engineering, 56, 7, 1803-1808, (2009)
[85] Manning, C.D.; Raghavan, P.; Schutze, H., An introduction to information retrieval, (2009), Cambridge University Press, (Online Edition)
[86] Marketa, Z.; Jeremy, O.B., Understanding bioinformatics, (2008), Garland Science · Zbl 1321.92017
[87] Maulik, U.; Mukhopadhyay, A., Simulated annealing based automatic fuzzy clustering combined with ANN classification for analyzing microarray data, Computers and operations research, 37, 8, 1369-1380, (2010) · Zbl 1183.68488
[88] McAllister, S.R.; DiMaggio, P.A.; Floudas, C.A., Mathematical modeling and efficient optimization methods for the distance-dependent rearrangement clustering problem, Journal of global optimization, 45, 1, 111-129, (2009) · Zbl 1179.90240
[89] Meng, J.; Gao, S.J.; Huang, Y., Enrichment constrained time-dependent clustering analysis for finding meaningful temporal transcription modules, Bioinformatics, 25, 12, 1521-1527, (2009)
[90] Merz, P., Analysis of gene expression profiles: an application of memetic algorithms to the minimum sum-of-squares clustering problem, Biosystems, 72, 1-2, 99-109, (2003)
[91] Mete, M.; Tang, F.; Xu, X.; Yuruk, N., A structural approach for finding functional modules from large biological networks, BMC bioinformatics, 9, S19, 1-14, (2008)
[92] Mitra, S.; Das, R.; Banka, H.; Mukhopadhyay, S., Gene interaction - an evolutionary biclustering approach, Information fusion, 10, 242-249, (2009)
[93] Monti, S.; Tamayo, P.; Mesirov, J.; Golub, T., Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data, Machine learning, 52, 1-2, 91-118, (2003) · Zbl 1039.68103
[94] Moretti, S., Statistical analysis of the Shapley value for microarray games, Computers and operations research, 2341, (2009)
[95] Mukhopadhyay, A.; Maulik, U., Towards improving fuzzy clustering using support vector machine: application to gene expression data, Pattern recognition, 42, 11, 2744-2763, (2009) · Zbl 1175.68382
[96] Nascimento, M.C.V.; Toledo, F.M.B.; Carvalho, A.C.P.L.F.D., Investigation of a grasp-based clustering algorithm applied to biological data, Computers and operations research, 37, 8, 1381-1388, (2010) · Zbl 1183.68494
[97] Newman, M.E.J., Analysis of weighted networks, Physical review E, 70, 056131, 1-9, (2004)
[98] Newman, M.E.J., Detecting community structure in networks, European physical journal B, 38, 2, 321-330, (2004)
[99] Newman, M.E.J., Finding community structure in networks using the eigen vectors of matrices, Physical review E, 74, 036104, (2006)
[100] Newman, M.E.J., Modularity and community structure in networks, Proceedings of the national Academy of sciences of the united states of America, 103, 23, 8577-8582, (2006)
[101] Newman, M.E.J.; Girvan, M., Finding and evaluating community structure in networks, Physical review E, 69, 026113, 1-15, (2006)
[102] Nueda, M.J.; Sebastián, P.; Tarazona, S.; García, F.G.; Dopazo, J.; Ferrer, A., Functional assessment of time course microarray data, BMC bioinformatics, 10, S9, 1-18, (2009)
[103] Palla, G.; Derényi, I.; Farkas, I.; Vicsek, T., Uncovering the overlapping community structure of complex networks in nature and society, Nature, 435, 9, 814-818, (2005)
[104] Phan, V.; George, E.O.; Tran, Q.T.; Goodwin, S., Analyzing microarray data with transitive directed acyclic graphs, Journal of bioinformatics and computational biology, 7, 1, 135-156, (2009)
[105] Qin, Z.S., Clustering microarray gene expression data using weighted Chinese restaurant process, Bioinformatics, 22, 16, 1988-1997, (2006)
[106] Ravi, V.; Bin, M.; Kumar, P.R., Threshold accepting based fuzzy clustering algorithms, International journal of uncertainty, fuzziness, and knowledge-based systems, 14, 5, 617-632, (2006) · Zbl 1114.68060
[107] Richards, A.L.; Holmans, P.; O’Donovan, M.C.; Owen, M.J.; Jones, L., A comparison of four clustering methods for brain expression microarray data, BMC bioinformatics, 9, 490, 1-17, (2008)
[108] Robbins, K.R.; Zhang, W.; Bertrand, J.K., The ant colony algorithm for feature selection in high-dimension gene expression data for disease classification, Mathematical medicine and biology, 24, 4, 413-426, (2007) · Zbl 1146.92318
[109] Rousseeuw, P.J., Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, Journal of computational and applied mathematics, 20, 53-65, (1987) · Zbl 0636.62059
[110] Ruan, J.; Zhang, W., Identifying network communities with a high resolution, Physical review E, 77, 016104, 1-12, (2008)
[111] Saha, S.; Bandyopadhyay, S., A new point symmetry based fuzzy genetic clustering technique for automatic evolution of clusters, Information sciences, 179, 19, 3230-3246, (2009) · Zbl 1193.68216
[112] Scharl, T.; Leisch, F., Gcexplorer: interactive exploration of gene clusters, Bioinformatics, 25, 8, 1089-1090, (2009)
[113] Scharl, T.; Voglhuber, I.; Leisch, F., Exploratory and inferential analysis of gene cluster neighborhood graphs, BMC bioinformatics, 10, 288, 1-14, (2009)
[114] Schwarz, A.J.; Gozzi, A.; Bifone, A., Community structure in networks of functional connectivity: resolving functional organization in the rat brain with pharmacological MRI, Neuroimage, 47, 1, 302-311, (2009)
[115] Shaik, Z.S.; Yeasin, M., A unified framework for finding differentially expressed genes from microarray experiments, BMC bioinformatics, 8, 347, 1-21, (2007)
[116] Sharma, A.; Podolsky, R.; Zhao, J.; McIndoe, R.A., A modified hyperplane clustering algorithm allows for efficient and accurate clustering of extremely large datasets, Bioinformatics, 25, 9, 1152-1157, (2009)
[117] Shen, R.; Olshen, A.B.; Ladanyi, M., Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis, Bioinformatics, 25, 22, 2906-2912, (2009)
[118] Steggies, L.J.; Banks, R.; Shaw, O.; Wipat, A., Qualitatively modeling and analyzing genetic regulatory networks: a Petri net approach, Bioinformatics, 23, 3, 336-343, (2007)
[119] Stone, E.A.; Ayroles, J.F., Modulated modularity clustering as an exploratory tool for functional genomic inference, Plos genetics, 5, 5, 1-13, (2009)
[120] Tan, M.P.; Broach, J.R.; Floudas, C.A., Evaluation of normalization and pre-clustering issues in a novel clustering approach: global optimum search with enhanced positioning, Journal of bioinformatics and computational biology, 5, 4, 895-913, (2007)
[121] Tan, M.P.; Broach, J.R.; Floudas, C.A., A novel clustering approach and prediction of optimal number of clusters: global optimum search with enhanced positioning, Journal of global optimization, 39, 3, 323-346, (2007) · Zbl 1149.90108
[122] Tan, M.P.; Smith, E.N.; Broach, J.R.; Floudas, C.A., Microarray data mining: a novel optimization-based approach to uncover biologically coherent structures, BMC bioinformatics, 9, 268, 1-21, (2008)
[123] Teboulle, M., A unified continuous optimization framework for center-based clustering methods, Journal of machine learning research, 8, 65-102, (2007) · Zbl 1222.68318
[124] Thalamuthu, A.; Mukhopadhyay, I.; Zheng, X.; Tseng, G.C., Evaluation and comparison of gene clustering methods in microarray analysis, Bioinformatics, 22, 19, 2405-2412, (2006)
[125] Tibely, G.; Kertesz, J., On the equivalence of the label propagation method of community detection and a Potts model approach, Physica A: statistical mechanics and its applications, 387, 19-20, 4982-4984, (2008)
[126] Torrente, A.; Kapushesky, M.; Brazma, A., A new algorithm for comparing and visualizing relationships between hierarchical and at gene expression data clusterings, Bioinformatics, 21, 21, 3993-3999, (2005)
[127] Tritchler, D.; Parkhomenko, E.; Beyene, J., Filtering genes for cluster and network analysis, BMC bioinformatics, 10, 193, 1-9, (2009)
[128] Tseng, G.C., Penalized and weighted k-means for clustering with scattered objects and prior information in high-throughput biological data, Bioinformatics, 23, 17, 2247-2255, (2007)
[129] Tseng, G.C.; Wong, W.H., Tight clustering: a resampling-based approach for identifying stable and tight patterns in data, Biometrics, 61, 1, 10-16, (2005) · Zbl 1077.62049
[130] Tu, Y.; Stolovitzky, G.; Klein, U., Quantitative noise analysis for gene expression microarray experiments, Proceedings of the national Academy of sciences of the united states of America, 99, 22, 14031-14036, (2002) · Zbl 1068.92021
[131] Tyler, A.L.; Asselbergs, F.W.; Williams, S.M.; Moore, J.H., Shadows of complexity: what biological networks reveal about epistasis and pleiotropy, Bioessays, 31, 2, 220-227, (2009)
[132] Wang, K.; Zheng, J.; Zhang, J.; Dong, J., Estimating the number of clusters via system evolution for cluster analysis of gene expression data, IEEE transactions on information technology in biomedicine, 13, 5, 848-853, (2009)
[133] Wang, S.; Zhu, J., Variable selection for model-based high-dimensional clustering and its application to microarray data, Biometrics, 64, 2, 440-448, (2008) · Zbl 1137.62041
[134] Wei, L.Y.; Cheng, C.H., An entropy clustering analysis based on genetic algorithm, Journal of intelligent and fuzzy systems, 19, 4-5, 235-241, (2008) · Zbl 1160.68534
[135] Wild, D.J.; Blankley, C.J., Comparison of 2d fingerprint types and hierarchy level selection methods for structural grouping using wards clustering, Journal of chemical information and computer sciences, 40, 155-162, (2000)
[136] Wu, F.X., Genetic weighted k-means algorithm for clustering large-scale gene expression data, BMC bioinformatics, 9, S12, 1-10, (2008)
[137] Xie, B.; Pan, W.; Shen, X., Variable selection in penalized model-based clustering via regularization on grouped parameters, Biometrics, 64, 3, 921-930, (2008) · Zbl 1146.62101
[138] Xu, Y.; Olman, V.; Xu, D., Clustering gene expression data using graph-theoretic approach: an application of minimum spanning trees, Bioinformatics, 18, 4, 536-545, (2002)
[139] Yeung KY, Fraley C, Murua A, Raftery AE, Ruzzo WL. Model-based clustering and data transformations for gene expression data. Tech Report, UW-CSE; 2001.
[140] Yeung, K.Y.; Haynor, D.R.; Ruzzo, W.L., Validating clustering for gene expression data, Bioinformatics, 17, 4, 309-318, (2001)
[141] Yip, A.M.; Ng, M.K.; Wu, E.H.; Chan, T.F., Strategies for identifying statistically significant dense regions in microarray data, IEEE/ACM transactions on computational biology and bioinformatics, 4, 3, 415-429, (2007)
[142] Yu, Z.; Wong, H.S., Class discovery from gene expression data based on perturbation and cluster ensemble, IEEE transactions on nanobioscience, 8, 2, 147-160, (2009)
[143] Yujin H, Philippe BJ, Pablo T, Golub TR, Mesirov JP. Subclass mapping: identifying common subtypes in independent disease data sets. PLoS One 2007;2(11).
[144] Zahoránszky, L.A.; Katona, G.Y.; Hári, P.; Csizmadia, A.M.; Zweig, K.A.; Köhalmi, G.Z., Breaking the hierarchy - a new cluster selection mechanism for hierarchical clustering methods, Algorithms for molecular biology, 4, 12, 1-22, (2009)
[145] Zhang, B.; Horvath, S., A general framework for weighted gene co-expression network analysis, Statistical applications in genetics and molecular biology, 4, 17, (2005) · Zbl 1077.92042
[146] Zhang, W.; Fang, H.B.; Song, J., Principal component tests: applied to temporal gene expression data, BMC bioinformatics, 10, S26, 1-9, (2009)
[147] Zhang, Y.; Xuan, J.; Reyes, B.G.D.L.; Clarke, R.; Ressom, H.W., Reverse engineering module networks by PSO-RNN hybrid modeling, BMC genomics, 10, S15, 1-10, (2009)
[148] Zhou, X.; Kao, M.C.J.; Wong, W.H., Transitive functional annotation by shortest-path analysis of gene expression data, Proceedings of the national Academy of sciences of the united states of America, 99, 20, 12783-12788, (2002)
[149] Zhu D, Dequeéant M-L, Li H. (2008) Comparative analysis of clustering methods for microarray data. In: Emmert-Streib F, Dehmer M, editors. Analysis of microarray data: a network-based approach, Weinheim, Germany; Wiley-VCH Verlag GmbH & Co. KGaA; doi:10.1002/9783527622818.ch2
[150] Zhu, D.; Hero, A.O.; Cheng, H.; Khanna, R.; Swaroop, A., Network constrained clustering for gene microarray data, Bioinformatics, 21, 21, 4014-4020, (2005)
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