article

Biclustering Algorithms for Biological Data Analysis: A Survey

First page image

Get full access to this article

Purchase, subscribe or recommend this article to your librarian.

References

  1. R. Agrawal J. Gehrke D. Gunopulus and P. Raghavan, “Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications,” Proc. ACM/SIGMOD Int'l Conf. Management of Data, pp. 94-105, 1998. Google ScholarGoogle Scholar
  2. 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 R. Levy W. Wilson M.R. Grever J.C. Byrd D. Botstein P.O. Brown and L.M. Staudt, “Distinct Types of Diffuse Large B-Cell Lymphoma Identified by Gene Expression Profiling,” Nature, vol. 403, pp. 503-511, 2000.Google ScholarGoogle Scholar
  3. U. Alon N. Barkai D.A. Notterman K. Gish S. Ybarra D. Mack and A.J. Levine, “Broad Patterns of Gene Expression Revealed by Clustering of Tumor and Normal Colon Tissues Probed by Oligonucleotide Arrays,” Natural Academy of Sciences, vol. 96,no. 12, pp. 6745-6750, 1999.Google ScholarGoogle Scholar
  4. S.A. Armstrong J.E. Staunton L.B. Silverman R. Pieters M.L. den Boer M.D. Minden S.E. Sallan E.S. Lander T.R. Golub and S.J. Korsmeyer, “Mll Translocations Specify a Distinct Gene Expression Profile that Distinguishes a Unique Leukemia,” Nature Genetics, vol. 30, pp. 41-47, 2002.Google ScholarGoogle Scholar
  5. P. Baldi and G.W. Hatfield, DNA Microarrays and Gene Expression. From Experiments to Data Analysis and Modelling. Cambridge Univ. Press, 2002.Google ScholarGoogle Scholar
  6. A. Ben-Dor B. Chor R. Karp and Z. Yakhini, “Discovering Local Structure in Gene Expression Data: The Order-Preserving Submatrix Problem,” Proc. Sixth Int'l Conf. Computational Biology (RECOMB '02), pp. 49-57, 2002. Google ScholarGoogle Scholar
  7. P. Berkhin and J.D. Becher, “Learning Simple Relations: Theory and Applications,” Proc. Second SIAM Int'l Conf. Data Mining, pp.nbsp420-436, 2002.Google ScholarGoogle Scholar
  8. S. Busygin G. Jacobsen and E. Kramer, “Double Conjugated Clustering Applied to Leukemia Microarray Data,” Proc. Second SIAM Int'l Conf. Data Mining, Workshop Clustering High Dimensional Data, 2002.Google ScholarGoogle Scholar
  9. A. Califano G. Stolovitzky and Y. Tu, “Analysis of Gene Expression Microarays for Phenotype Classification,” Proc. Int'l Conf. Computacional Molecular Biology, pp. 75-85, 2000. Google ScholarGoogle Scholar
  10. Y. Cheng and G.M. Church, “Biclustering of Expression Data,” Proc. Eighth Int'l Conf. Intelligent Systems for Molecular Biology (ISMB '00), pp. 93-103, 2000. Google ScholarGoogle Scholar
  11. H. Cho I.S. Dhillon Y. Guan and S. Sra, “Minimum Sum-Squared Residue Cococlustering of Gene Expression Data,” Proc. Fourth SIAM Int'l Conf. Data Mining, 2004.Google ScholarGoogle Scholar
  12. R.J. Cho M.J. Campbell E.A. Winzeler L. Steinmetz A. Conway L. Wodicka T.G. Wolfsberg A.E. Gabrielian D. Landsman D.J. Lockhart and R.W. Davis, “A Genome-Wide Transcriptional Analysis of the Mitotic Cell Cycle,” Molecular Cell, vol. 2, pp. 65-73, 1998.Google ScholarGoogle Scholar
  13. T.H. Cormen C.E. Leiserson R.L. Rivest and C. Stein, Introduction to Algorithms, The MIT Electrical Eng, and Computer Science Series, The MIT Press, second ed., 2001. Google ScholarGoogle Scholar
  14. I.S. Dhillon, “Co-Clustering Documents and Words Using Bipartite Spectral Graph Partitioning,” Proc. Seventh ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '01), pp. 269-274, 2001. Google ScholarGoogle Scholar
  15. I.S. Dhillon S. Mallela and D.S. Modha, “Information-Theoretical Coclustering,” Proc. Ninth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '03), pp. 89-98, 2003. Google ScholarGoogle Scholar
  16. D. Duffy and A. Quiroz, “A Permutation Based Algorithm for Block Clustering,” J. Classification, vol. 8, pp. 65-91, 1991.Google ScholarGoogle Scholar
  17. N. Friedman and M. Goldszmidt, “Learning Bayesian Networks with Local Structure,” Learning in Graphical Models, Kluwer, pp.nbsp421-460, 1998. Google ScholarGoogle Scholar
  18. A.P. Gasch M. Huang S. Metzner D. Botstein S.J. Elledge and P.O. Brown, “Genomic Expression Responses to DNA-Damaging Agents and the Regulatory Role of the Yeast ATR Homolog mec1p,” Molecular Biology of the Cell, vol. 12, pp. 2987-3003, 2001.Google ScholarGoogle Scholar
  19. A.P. Gasch P.T. Spellman C.M. Kao O. Carmel-Harel M.B. Eisen G. Storz D. Botstein and P.O. Brown, “Genomic Expression Programs in the Response of Yeast Cells to Environmental Changes,” Molecular Biology of the Cell, vol. 11, pp. 4241-4257, 2000.Google ScholarGoogle Scholar
  20. W. Gaul and M. Schader, “A New Algorithm for Two-Mode Clustering,” Data Analysis and Information Systems, H. Hermann and W. Polasek, eds., Springer, pp. 15-23, 1996.Google ScholarGoogle Scholar
  21. G. Getz E. Levine and E. Domany, “Coupled Two-Way Clustering Analysis of Gene Microarray Data,” Proc. Natural Academy of Sciences US, pp. 12079-12084, 2000.Google ScholarGoogle Scholar
  22. T.R. Golub D.K. Slonim P. Tamayo C. Huard M. Gaasenbeek J.P. Mesirov H. Coller M.L. Loh J.R. Downing M.A. Caligiuri C.D. Bloomfield and E.S. Lander, “Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring,” Science, vol. 286, pp. 531-537, 1999.Google ScholarGoogle Scholar
  23. D. Gusfield, Algorithms on Strings, Trees, and Sequences. Computer Science and Computational Biology Series, Cambridge Univ. Press, 1997. Google ScholarGoogle Scholar
  24. J.A. Hartigan, “Direct Clustering of a Data Matrix,” J. Am. Statistical Assoc. (JASA), vol. 67, no. 337, pp. 123-129, 1972.Google ScholarGoogle Scholar
  25. I. Hedenfalk D. Duggan Y. Chen M. Radmacher M. Bittner R. Simon P. Meltzer B. Gusterson M. Esteller M. Raffeld Z. Yakhini A. Ben-Dor E. Dougherty J. Kononen L. Bubendorf W. Fehrle S. Pittaluga S. Gruvberger N. Loman O. Johannsson H. Olsson B. Wilfond G. Sauter O.P. Kallioniemi A. Borg and J. Trent, “Gene-Expression Profiles in Hereditary Breast Cancer,” New England J. Medicine, vol. 344, no. 8, pp. 539-548, 2000.Google ScholarGoogle Scholar
  26. J. Hipp U. Güntzer and G. Nakhaeizadeh, “Algorithms for Association Rule Mining-A General Survey and Comparison,” SIGKDD Explorations, vol. 2, no. 1, pp. 58-64, July 2000. Google ScholarGoogle Scholar
  27. T. Hofmann and J. Puzicha, “Latent Class Models for Collaborative Filtering,” Proc. Int'l Joint Conf. Artificial Intelligence, pp. 668-693, 1999. Google ScholarGoogle Scholar
  28. T.R. Hughes M.J. Marton A.R. Jones C.J. Roberts R. Stoughton C.D. Armour H.A. Bennett E. Coffey H. Dai Y.D. He M.J. Kidd A.M. King M.R. Meyer D. Slade P.Y. Lum S.B. Stepaniants D.D. Shoemaker D. Gachotte K. Chakraburtty J. Simon M. Bard and S.H. Friend, “Functional Discovery via a Compendium of Expression Profiles,” Cell, vol. 102, pp. 109-126, 2000.Google ScholarGoogle Scholar
  29. T. Ideker V. Thorsson J.A. Ranish R. Christmas J. Buhler J.K. Eng R. Bumgarner D.R. Goodlett ? Aebersold and L. Hood, “Integrated Genomic and Proteomic Analyses of a Systematically Perturbed Metabolic Network,” Science, vol. 292, pp. 929-934, 2001.Google ScholarGoogle Scholar
  30. V.R. Iyer M.B. Eisen D.T. Ross G. Schuler T. Moore J.C.F. Lee J.M. Trent L.M. Staudt J. Hudson Jr. M.S. Boguski D. Lashkari D. Shalon D. Botstein and P.O. Brown, “The Transcriptional Program in the Response of Human Fibroblasts to Serum,” Science, vol. 283, pp. 83-87, 1999.Google ScholarGoogle Scholar
  31. U. Klein Y. Tu G.A. Stolovitzky M. Mattioli G. Cattoretti H. Husson A. Freedman G. Inghirami L. Cro L. Baldini A. Neri A. Califano and R. Dalla-Favera, “Gene Expression Profiling of B-Cell Chronic Lymphocytic Leukemia Reveals a Homogeneous Phenotype Related to Memory B Cells,” J. Experimental Medicine, vol. 194, pp. 1625-1638, 2001.Google ScholarGoogle Scholar
  32. Y. Klugar R. Basri J.T. Chang and M. Gerstein, “Spectral Biclustering of Microarray Data: Coclustering Genes and Conditions,” Genome Research, vol. 13, pp. 703-716, 2003.Google ScholarGoogle Scholar
  33. U. Kluger B. Kacinski Y. Kluger O. Mironenko M. Gilmore-Hebert J. Chang A. Perkins and E. Sapi, “Microarray Analysis of Invasive and Metatastic Phenotypes in a Breast Cancer Model,” Poster Presented at the Gordon Conf. Cancer, 2001.Google ScholarGoogle Scholar
  34. L. Lazzeroni and A. Owen, “Plaid Models for Gene Expression Data,” technical report, Stanford Univ., 2000.Google ScholarGoogle Scholar
  35. J. Liu and W. Wang, “OP-Cluster: Clustering by Tendency in High Dimensional Space,” Proc. Third IEEE Int'l Conf. Data Mining, pp.nbsp187-194, 2003. Google ScholarGoogle Scholar
  36. B. Mirkin, “Nonconvex Optimization and its Applications,” Math. Classification and Clustering, Kluwer Academic Publishers, 1996.Google ScholarGoogle Scholar
  37. T.M. Murali and S. Kasif, “Extracting Conserved Gene Expression Motifs from Gene Expression Data,” Proc. Pacific Symp. Biocomputing, vol. 8, pp. 77-88, 2003.Google ScholarGoogle Scholar
  38. R. Peeters, “The Maximum Edge Biclique Problem is NP-Complete,” Discrete Applied Math., vol. 131, no. 3, pp. 651-654, 2003. Google ScholarGoogle Scholar
  39. S.L. Pomeroy P. Tamayo M. Gaasenbeek L.M. Sturla M. Angelo M.E. McLaughlin J.Y. Kim L.C. Goumnerova P.M. Black C. Lau J.C. Allen D. Zagzag J.M. Olson T. Curran C. Wetmore J.A. Biegel T. Poggio S. Mukherjee R. Rifkin A. Califano G. Stolovitzky D.N. Louis J.P. Mesirov E.S. Lander and T.R. Golub, “Prediction of Central Nervous System Embryonal Tumour Outcome Based on Gene Expression,” Nature, vol. 415, no. 6870, pp. 436-442, 2002.Google ScholarGoogle Scholar
  40. E. Segal A. Battle and D. Koller, “Decomposing Gene Expression into Cellular Processes,” Proc. Pacific Symp. Biocomputing, vol. 8, pp. 89-100, 2003.Google ScholarGoogle Scholar
  41. E. Segal B. Taskar A. Gasch N. Friedman and D. Koller, “Rich Probabilistic Models for Gene Expression,” Bioinformatics, vol. 17, pp. S243-S252, 2001.Google ScholarGoogle Scholar
  42. Q. Sheng Y. Moreau and B. De Moor, “Biclustering Microarray Data by Gibbs Sampling,” Bioinformatics, vol. 19, pp. ii196-ii205, 2003.Google ScholarGoogle Scholar
  43. P.T. Spellman G. Sherlock M.Q. Zhang V.R. Iyer K. Anders M.B. Eisen P.O. Brown D. Botstein and B. Futcher, “Comprehensive Identification of Cell Cycle-Regulated Genes of the Yeast Saccharomyces Cerevisiae by Microarray Hybridization,” Molecular Biology of the Cell, vol. 9, pp. 3273-3297, 1998.Google ScholarGoogle Scholar
  44. A. Tanay R. Sharan and R. Shamir, “Discovering Statistically Significant Biclusters in Gene Expression Data,” Bioinformatics, vol. 18, pp. S136-S144, 2002.Google ScholarGoogle Scholar
  45. C. Tang L. Zhang I. Zhang and M. Ramanathan, “Interrelated Two-Way Clustering: An Unsupervised Approach for Gene Expression Data Analysis,” Proc. Second IEEE Int'l Symp. Bioinformatics and Bioeng., pp. 41-48, 2001. Google ScholarGoogle Scholar
  46. R. Tibshirani T. Hastie M. Eisen D. Ross D. Botstein and P. Brown, “Clustering Methods for the Analysis of DNA Microarray Data,” technical report, Dept. of Health Research and Policy, Dept. of Genetics, and Dept. of Biochemestry, Stanford Univ., 1999.Google ScholarGoogle Scholar
  47. L. Ungar and D.P. Foster, “A Formal Statistical Approach to Collaborative Filtering,” Proc. Conf. Automated Learning and Discovery (CONALD '98), 1998.Google ScholarGoogle Scholar
  48. H. Wang W. Wang J. Yang and P.S. Yu, “Clustering by Pattern Similarity in Large Data Sets,” Proc. 2002 ACM SIGMOD Int'l Conf. Management of Data, pp. 394-405, 2002. Google ScholarGoogle Scholar
  49. J.N. Weinstein T.G. Myers P.M. O'Connor S.H. Friend A.J. Fornace Jr. K.W. Kohn T. Fojo S.E. Bates L.V. Rubinstein N.L. Anderson J.K. Buolamwini W.W. Van Osdol A.P. Monks D.A. Scudiero E.A. Sausville D.W. Zaharevitz B. Bunow V.N. Viswanadhan G.S. Johnson R.E. Wittes and K.D. Paull, “An Information-Intensive Approach to the Molecular Pharmacology of Cancer,” Science, vol. 275, pp. 343-349, 1997.Google ScholarGoogle Scholar
  50. J. Yang W. Wang H. Wang and P. Yu, “δ-Clusters : Capturing Subspace Correlation in a Large Data Set,” Proc. 18th IEEE Int'l Conf. Data Eng., pp. 517-528, 2002. Google ScholarGoogle Scholar
  51. J. Yang W. Wang H. Wang and P. Yu, “Enhanced Biclustering on Expression Data,” Proc. Third IEEE Conf. Bioinformatics and Bioeng., pp. 321-327, 2003. Google ScholarGoogle Scholar
  52. V. Yong S. Chabot Q. Stuve and G. Williams, “Interferon Beta in the Treatment of Multiple Sclerosis: Mechanisms of Action,” Neurology, vol. 51, pp. 682-689, 1998.Google ScholarGoogle Scholar

Index Terms

(auto-classified)
  1. Biclustering Algorithms for Biological Data Analysis

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        About Cookies On This Site

        We use cookies to ensure that we give you the best experience on our website.

        Learn more

        Got it!