Paradigm of Tunable Clustering Using Binarization of Consensus Partition Matrices (Bi-CoPaM) for Gene Discovery

PLoS ONE

Volumn 8, Issue 2, 2013, Pages

  Abu Jamous, Basel ^a   Fa, Rui ^a   Roberts, David J ^b,c   Nandi, Asoke K ^a,d  

^a UNIVERSITY OF LIVERPOOL   (United Kingdom)

^b NHS BLOOD AND TRANSPLANT   (United Kingdom)

^c UNIVERSITY OF OXFORD   (United Kingdom)

^d UNIVERSITY OF JYVÄSKYLÄ   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BINARIZATION OF CONSENSUS PARTITION MATRIX; CELL CYCLE; CLUSTER ANALYSIS; DNA MICROARRAY; FUNGAL GENE; FUZZY SYSTEM; GENE CLUSTER; GENE DISCOVERY; GENETIC ALGORITHM; GENETIC ANALYSIS; STATISTICAL CONCEPTS; VALIDITY;

ALGORITHMS; CELL CYCLE; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; GENES, FUNGAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; YEASTS;

EID: 84873692197     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0056432     Document Type: Article

References (45)

1. Gibbons FD, Roth FP, (2002) Judging the quality of gene expression-based clustering methods using gene annotation. Genome Research 12: 1574-1581.
2. Haykin S, (1999) Neural Networks - A Comprehensive Foundation, 3rd edition. Singapore: Pearson, Prentice Hall.
3. Xiao X, Dow ER, Eberhart R, Miled ZB, Oppelt RJ (2003) Gene clustering using self-organizing maps and particle swarm optimization. In IEEE Parallel and Distributed Processing Symposium Proceedings; Indianapolis. 154-163.
4. Kohonen TE, (1997) Self-Organizing Maps. New York: Springer-Verlag.
5. Pena JM, Lozano JA, Larranaga P, (1999) An empirical comparison of four initialization methods for the K-Means algorithm. Pattern Recognition Letters 20: 1027-1040.
6. Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. In Proc. Natl. Acad. Sci. 14863-14868.
7. Rhouma MBH, Frigui H, (2001) Self-organization of pulse-coupled oscillators with application to clustering. IEEE Trans. Pattern Anal. Mach. Intell. 23: 1-16.
8. Salem SA, Jack LB, Nandi AK, (2008) Investigation of self-organizing oscillator networks for use in clustering microarray data. IEEE Trans. Nanobioscience 7: 65-79.
9. Baumgartner R, Windischberger C, Moser E, (1998) Quantification in functional magnetic resonance imaging: fuzzy clustering vs. correlation analysis. Magnetic Resonance Imaging 16: 115-125.
10. Slonim N, Atwal GS, Tkacik G, Bialek W, (2005) Information-based clustering. Proc Natl Acad Sci USA 102: 18297-18302.
11. Vega-Pons S, Ruiz-Shulcloper J, (2011a) A survey of clustering ensemble algorithms. International Journal of Pattern Recognition and Artifcial Intelligence 25: 337-372.
12. Weingessel A, Dimitriadou E, Hornik K, (2003) An ensemble method for clustering. In Distributed Statistical Computing Working Papers; Vienna.
13. Tumer K, Agogino AK, (2008) Ensemble clustering with voting active clusters. Pattern Recognition Letters 29: 1947-1953.
14. Ayad HG, Kamel MS, (2010) On voting-based consensus of cluster ensembles. Pattern Recognition 43: 1943-1953.
15. Fred A, Jain AK (2002) Data clustering using evidence accumulation. In Proceedings of the Sixteenth International Conference on Pattern Recognition (ICPR). 276-280.
16. Strehl A, Ghosh J, (2002) Cluster ensembles - A knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research 3: 583-617.
17. Fern XZ, Brodley CE (2004) Solving cluster ensemble problems by bipartite graph partitioning. In Proceedings of the Twenty-first International Conference on Machine Learning; New York, NY.
18. Yu Z, Wong HS, Wang H, (2007) Graph-based consensus clustering for class discovery from gene expression data. Bioinformatics 23: 2888-2896.
19. Vega-Pons S, Correa-Morris J, Ruiz-Shulcloper J (2008) Weighted cluster ensemble using a kernel consensus function. In Lecture Notes in Computer Science. 5197. Heidelberg: Springer p. 195-202.
20. Vega-Pons S, Ruiz-Shulcloper J (2009) Clustering ensemble method for heterogeneous partitions. In CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. 481-488.
21. Vega-Pons S, Ruiz-Shulcloper J, Guerra-Gandón A, (2011b) Weighted association based methods for the combination of heterogeneous partitions. Pattern Recognition Letters 32: 2163-2170.
22. Filkov V, Skiena S (2003) Integrating microarray data by consensus clustering. In Proc. of International Conference on Tools with Artificial Intelligence; Davis, CA. 418-426.
23. Wang W (2010) An improved non-negative matrix factorization algorithm for combining multiple clusterings. In 2010 International Conference on Machine Vision and Human-machine Interface; Kaifeng, China. 604-607.
24. Greene D, Cagney G, Krogan N, Cunningham P, (2008) Ensemble non-negative matrix factorization methods for clustering protein-protein interactions. Bioinformatics 24: 1722-1728.
25. Avogadri R, Valentini G, (2008) Ensemble clustering with a fuzzy approach. In Supervised and Unsupervised Ensemble Methods and their Applications Studies in Computational Intelligence. 126. Edited by Okun, O. Berlin: Springer-Verlag.
26. Zhou X, Mao KZ, (2005) LS bound based gene selection for DNA microarray data. Bioinformatics 21: 1559-1564.
27. Abu-Jamous B, Fa R, Nandi AK, Roberts DJ, (2012) Binarization of consensus partition matrix for ensemble clustering. In Proceedings of the 20th European Signal Processing Conference (EUSIPCO-2012); Bucharest pp. 2193-2197.
28. Abu-Jamous B, Fa R, Roberts DJ, Nandi AK, (2012) Comprehensive analysis of multiple microarray datasets by binarization of consensus partition matrix. In 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2012); Santander, Spain.
29. Babusïka R, Alic L, Lourens MS, Verbraak AFM, Bogaard J, (2001) Estimation of respiratory parameters via fuzzy clustering. Artificial Intelligence in Medicine 21: 91-105.
30. Nwamadi O, Zhu X, Nandi AK, (2011) Dynamic physical resource block allocation algorithms for uplink long term evolution. IET Commun. 5: 1020-1027.
31. Yeung KY, Fraley C, Murua A, Raftery AE, Ruzzo WL, (2001) Model-based clustering and data trasformations for gene expression data. Bioinformatics 17: 977-987.
32. Zhao LP, Presntice R, Breeden L, (2001) Statistical modelling of large microarray data sets to identify stimulus-response profiles. PNAS 98: 5631-5636.
33. Liu D, Umbach DM, Peddada SD, Li L, Crockett PW, et al. (2004) A random-periods model for expression of cell-cycle genes. PNAS 11: 7240-7245.
34. Cho RJ, Campbell MJ, Winzeler EA, Steinmetz L, Conway A, Wodicka L, Wolfsberg TG, Gabrielian AE, Landsman D, Lockhart DJ, et al. (1998) A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell 2: 65-73.
35. Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, et al. (1998) Comprehensive Identification of Cell Cycle-regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization. Molecular Biology of the Cell 9: 3273-3297.
36. Pramila T, Wu W, Miles S, Noble WS, Breeden LL, (2006) The Forkhead transcription factor Hcm1 regulates chromosome segregation genes and fills the S-phasegap in the transcriptional circuitryof the cell cycle. Genes and Development 20: 2266-2278.
37. Fa R, Nandi AK, (2011) Parametric validity index of clustering for microarray gene expression data. In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing 2011.
38. Boor Cd (1978) A Practical Guide to Splines. New York: Springer-Verlag.
39. Dimitriadou E, Weingessel A, Hornik K, (2002) A combination scheme for fuzzy clustering. International Journal of Pattern Recognition and Artificial Intelligence 16.
40. Dimitriadou E, Weingessel A, Hornik K, (2002) A combination scheme for fuzzy clustering. In Proceedings of the 2002 AFSS International Conference on Fuzzy Systems; Calcutta pp. 332-338.
41. Dudoit S, Fridlyand J, (2003) Bagging to improve the accuracy of a clustering procedure. Bioinformatics 19: 1090-1099.
42. Fischer B, Buhmann JM, (2003) Bagging for Path-Based Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 25: 1411-1415.
43. Kuhn H (1955) The hungarian method for the assignment problem. Naval Research Logistics Quarterly 2.
44. Stuart D, Wittenberg C, (1995) CLN3, not positive feedback, determines the timing of CLN2 transcription in cycling cells. Genes ad Development 9: 2780-2794.
45. Nilsson R, Schultz IJ, Pierce EL, Soltis KA, Naranuntarat A, et al. (2009) Discovery of Genes Essential for Heme Biosynthesis through Large-Scale Gene Expression Analysis. Cell Metabolism 10: 119-130.