Microarray image analysis: An algorithmic approach

Microarray Image Analysis: An Algorithmic Approach

Volumn , Issue , 2010, Pages 1-312

  Fraser, Karl ^a   Wang, Zidong ^b   Liu, Xiaohui ^b  

^a Centre for Intelligent Data Analysis (CIDA)   (United Kingdom)

^b BRUNEL UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BIOASSAY; DATA HANDLING; GENE EXPRESSION; IMAGE ENHANCEMENT; MAXIMUM PRINCIPLE; MEDICAL IMAGING; MOLECULAR BIOLOGY;

DNA MICROARRAY TECHNOLOGY; EXPECTATION-MAXIMIZATION ALGORITHMS; FEATURE IDENTIFICATION; GENE EXPRESSION TIME SERIES; GENE REGULATORY NETWORKS; MICROARRAY IMAGE ANALYSIS; MICROARRAY IMAGE PROCESSING; TRADITIONAL TECHNIQUES;

IMAGE ANALYSIS;

EID: 85054247045     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/9781420091540     Document Type: Book

References (231)

1. Adams R and Bischof L. Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16:641-647, 1994.
2. Affymetrix Corporation. http://www.affymetrix.com/index.affx, 2005.
3. Agrawal R, Gehrke J, Gunopulos D, and Raghavan P. Automatic subspace clustering of high dimensional data for data mining applications. In Proceedings of The ACM SIGMOD International Conference on Management of Data, pages 94-105, New York: ACM Press, June 1998.
4. Agarwala A, Dontcheva M, Agrawala M, Drucker S, Colburn A, Curless B, Salesin D, and Cohen M. Interactive digital photomontage. ACM Transactions on Graphics, 23(3):294-302, 2004.
5. Akutsu T, Miyano S, and Kuhara S. Identification of genetic networks from a small number of gene expression patterns under the boolean network model, Proc. of Pacific Symposium on Biocomputing, 4:17-28, 1999.
6. Anderson BDO and Moore JB. Optimal Filtering. Englewood Cliffs, NJ: Prentice Hall, 1979.
7. Antoniol G and Ceccarelli M. A Markov random field approach to microarray image gridding. In: Proceedings of the 17th International Conference on Pattern Recognition, 3:550-553, 2004.
8. Antoniol G, Ceccarelli M, and Petrosino A. Microarray image addressing based on the Radon transform. In Proceedings of IEEE International Conference on Image Processing, 1:13-16, 2005.
9. Ahuja N, Rosenfeld A, and Haralick RM. Neighbour gray levels as features in pixel classification. Pattern Recognition, 12:251-260, 1980.
10. Anderberg MR. Cluster Analysis for Applications. New York: Academic Press Inc, 1973.
11. Angulo J and Serra J. Automatic analysis of DNA microarray images using mathematical morphology. Bioinformatics, 19(5):553-562, 2003.
12. Appleton B and Talbot H. Globally optimal geodesic active contours. Journal of Mathematical Imaging and Vision, 23(1):67-86, 2005.
13. Arabidopsis GI. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature, 408:796-815, 2000.
14. Axon. GenePix c© Pro 6.0 Microarray image analysis. http://www.mole-culardevices.com/pages/software/gn-genepix-pro.html, 2005.
15. Axon. Acuity c© 4.0 Gene expression analysis. http://www.molecular-devices.com/pages/software/gn-acuity.html, 2005.
16. Bajcsy P. Gridline: automatic grid alignment in DNA microarray scans. IEEE Transactions on Image Processing, 13(1):15-25, 2004.
17. Bajcsy P. An overview of DNA microarray grid alignment and foreground separation approaches. EURASIP Journal on Applied Signal Processing, 2006:1-13, Article ID 80163, 2006.
18. Balasubramaniyan R, Hüllermeier E, Weskamp N, and Kämper J. Clustering of gene expression data using a local shape-based similarity measure. Bioinformatics, 21(7):1069-1077, 2005.
19. Ball GB. Data analysis in the social sciences: what about the details? In Procedures of Joint Computing Conference, pages 533-559, Washington, DC: Spartan Books, 1965.
20. Barrett WA and Cheney AS. Object-based image editing. In: Proceedings of the 29th conference on Computer graphics and interactive techniques, 777-784, San Antonio, July 2002.
21. Baugh LR, Hill AA, Claggett JM, Hill-Harfe K, Wen JC, Slonim DK, Brown EL and Hunter CP. The homeodomain protein PAL-1 specifies a lineage-specific regulatory network in the C. elegans embryo, Development, 132:1843-1854, 2005.
22. Beal MJ, Falciani F, Ghahramani Z, Rangel C, and Wild DL. A Bayesian approach to reconstructing genetic regulatory networks with hidden factors, Bioinformatics, October, 2004.
23. Beare R and Buckley M. “Spot.” http://spot.cmis.csiro.au/spot/doc/Spot.pdf.
24. Berkhin P. Survey of Clustering Data Mining Techniques. Accrue Software, San Jose, CA, 2002.
25. Bertalmio M, Bertozzi A, and Sapiro G. Navier-stokes, fluid dynamics, and image and video inpainting. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pages 355-362, Dec. 2001.
26. Beucher S and Meyer F. The morphological approach to segmentation: the watershed transformation. In Mathematical Morphology in Image Processing, Chapter 12, 433-481, New York: Marcel Dekker, 1993.
27. Bhanu B and Faugeras O. Segmentation of images having unimodal distributions. IEEE Transactions on Pattern Analysis Machine Learning and Intelligence, 4:408-419, 1982.
28. BioDiscovery. AutoGene, http://www.biodiscovery.com, 2001.
29. Biran I, Walt DR, and Epstein J. Fluorescence-based nucleic acid detection and microarrays. Anal. Chim. Acta, 496(1):3-36, 2002.
30. Blekas K, Galatsanos NP, and Georgiou I. An unsupervised artifact correction approach for the analysis of DNA microarray images. In: Proceedings of International Conference on Image Processing, 2:165-168, 2003.
31. Bozdech Z, Llinas M, Pulliam B, Wong ED, and Zhu J. The transcriptome of the intraerythrocytic developmental cycle of plasmodium falciparum, PLoS Biology, 1(1):85-100, 2003.
32. Bozinov D and Rahenführer J. Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering. Bioinformatics, 18(5):747-756, 2002.
33. Bozinov D. Autonomous system for web based microarray image analysis. IEEE Transactions on Nanobioscience, 2(4):215-220, 2003.
34. Brandle N, Bischof H, and Lapp H. A generic and robust approach for the analysis of spot array images. In Proceedings of SPIE in progress in biomedical optics and imaging: microarrays: optical technologies and informatics, 4266:1-12, San Jose, CA, January 2001.
35. Brandle N, Chen HY, Bischof H, and Lapp H. Robust parametric and semi-parametric spot fitting for spot array images. In Proceedings 8th Intel Systems for molecular biology, 20-23:46-56, La Jolla, CA, August 2000.
36. Brandle N, Lapp H, and Bischof H. Fully Automatic grid fitting for genetic spot array images containing guide spots. ReportTechnical Report, PRIP-TR-58, Vienna University of Technology, Austria, 2000.
37. Buhler J, Ideker T, and Haynor D. Dapple: improved techniques for finding spots on DNA microarrays. UV CSE Technical Report, UWTR, 2000.
38. Ceccarelli M and Antoniol G. A deformable grid-matching approach for microarray images. IEEE Trans Image Process., 15(10):3178-3188, 2006.
39. Chan T, Kang S, and Shen J. Euler’s elastica and curvature based in-paintings. Journal of Applied Mathematics, 63(2):564-592, 2002.
40. Chargaff E. Chemical specificity of nucleic acids and mechanism of their enzymic degradation. Experientia, 6:201-209, 1951.
41. Chargaff E. Structure and function of nucleic acids as cell constituents. Fed. Proceedings, 10:654-659, 1951.
42. Chatfield C. The Analysis of Time Series: an Introduction, 6th Edition, Chapman and Hall, 2004.
43. Chen T, He HL, and Church GM. Modeling gene expression with differential equations, Proc. of Pacific Symposium on Biocomputing, 4:29-40, 1999.
44. Chen Y, Dougherty ER, and Bittner ML. Ratio-based decisions and the quantitative analysis of cDNA microarray images. Journal of Biomedical Optics, 2(4):364-374, 1997.
45. Cheriet M, Said JN, and Suen CY. A recursive thresholding technique for image segmentation. IEEE Transactions on Image Processing, 7(6):918-920, 1998.
46. Churchill GA. Fundamentals of experimental design for cDNA microarrays. Nature Genetics, 32:490-495, 2002.
47. Coe B. You want Ketchup with your DNA chips? An overview of expression microarrays. BioTeach Online Journal, 1(1):89-94, 2003.
48. Cook DL, Gerber AN, and Tapscott SJ. Modeling stochastic gene expression: implications for haploinsufficiency. Proceedings of the National Academy of Science, USA, 95:15641-15646, 1998.
49. Crick FH. On protein synthesis. Symp. Soc. Exp. Biol., 12:138-163, 1958.
50. Crick FH. Central dogma of molecular biology. Nature, 227:561-563, 1970.
51. Crick HF, Barnett L, Brenner S, and Watts-Tobin RJ. General nature of genetic code for proteins. Nature, 192:1227-1232, 1961.
52. CSIRO. SPOT Image Analysis Software. Mathematical and Informational Sciences, http://experimental.act.cmis.csiro.au/spot/index.php, 2005.
53. Dave RN. Generalized fuzzy C-shells clustering and detection of circular and elliptical boundaries. Pattern Recognition, 25(1):713-722, 1992.
54. Daxin J, Chun T, and Aidong Z. Cluster analysis for gene expression data: a survey. IEEE Transactions on Knowledge and Data Engineering, 16(11):1370-1386, 2004.
55. de Hoon MJ, Imoto S, Kobayashi K, Ogasawara N, and Miyano S. Inferring gene regulatory networks from time-ordered gene expression data of bacillus subtilis using differential equations, Proc. of Pacific Symposium on Biocomputing, 17-28, 2003.
56. de Jong H. Modeling and simulation of genetic regulatory systems: a literature review, J Comput Biol, 9(1):67-103, 2002.
57. Deng N and Duan H. An automatic and power spectra-based rotate correcting algorithm for microarray image. Proceedings of IEEE Eng Med Biol Soc Conference, 1:898-901, 2005.
58. D’haeseleer P, Wen X, Fuhrman S, and Somogyi R. Linear modeling of mRNA expression levels during CNS development and injury, Proc. of Pacific Symposium on Biocomputing, 41-52, 1999.
59. Diday E and Simon JC. Clustering analysis. Digital Pattern Recognition, 1(1):47-94, Berlin: Springer-Verlag, 1976.
60. Diggle P. Time Series: a Biostatistical Introduction, Oxford Statistical Science Series 5, 1990.
61. DeRisi JL, Iyer VR, and Brown PO. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science, 278(5338):680-686, 1996.
62. DeRisi JL, Penland L, Brown PO, Bittner ML, Meltzer PS, Ray M, Chen YD, Su YA, and Trent JM. Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nature Genetics, 14(4):457-460, 1996.
63. Dubes RC. Cluster Analysis and Related Issues, 1st ed. River Edge, NJ: World Scientific Publishing Co. Inc., 1993.
64. Dumochel W. Data squashing: constructing summary datasets. In Abell OJ, Pardalos P, and Resende GC, editors, Handbook of Massive Datasets, 1st ed. 579-592, Amsterdam, Kluwer Academic Publishers, 2002.
65. Dumochel W, Volinsky C, Johnson T, Cortes C, and Pregibon D. Squashing flat files flatter. In Proceedings of the 5th ACM Conference on Knowledge Discovery and Data Mining, 6-15, New York: ACM Press, August 1999.
66. Dunn CJ. A fuzzy relative of ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3(3):32-57, 1974.
67. Duyk GM. Sharper tools and simpler methods. Nature Genetics, 32:474-479, 2002.
68. Efros AA and Leung TK. Texture synthesis by non-parametric sampling. In Proceedings of the IEEE International Conference on Computer Vision, 1033-1038, 1999.
69. Eisen M. ScanAlyse. http://rana.lbl.gov/EisenSoftware.htm, 2002.
70. Eisen M, Spellman PT, Brown PO, and Botstein D. Cluster analysis and display of genome-wide expression Ppatterns. In Proceedings of the National Academy of Sciences, 14863-14868, Washington, DC, December 1998.
71. Ekins RP. UK Patent Application 8803000, 1987.
72. Ekins RP, Chu R, and Biggart E. The development of microspot, multianalyte radiometric immunoassay using dual fluorescent-labelled antibodies. Anal. Chim. Acta., 227:73-96, 1989.
73. Ekins RP and Chu FW. Multianalyte microspot immunoassay-microanalytical “compact disk” of the future. Clinical Chemistry, 37:1955-1967, 1991.
74. Ekins R, Chu F, and Micallef. J. High specific activity chemiluminescent and fluorescent markers: their potential application to high sensitivity and “multianalyte” immunoassays. J. Journal Biolumin. Chemilumin, 4:59-78, 1989.
75. Epstein J, Leung A, Lee K, and Walt DR. High-density, microsphere-based fiber optic DNA microarrays. Biosensors and Bioelectronics, 5-6(18):541-546, 2003.
76. Fabbri R, da Costa LF, and Barrera J. Towards non-parametric gridding of microarray images. In 14th International Conference on Digital Signal Processing, 2:623-626, 2002.
77. Fleischmann RD, Adams MD, White O, Clayton RA, Kirkness EF, Kerlavage AR, and Bult CJ. Whole genome random sequencing and assembly of Haemophilus influenzae Rd. Science, 269:496-512, 1995.
78. Fodor SP, Read JL, Pirrung MC, Stryer L, Lu AT, and Solas D. Light-directed, spatially addressable parallel chemical synthesis. Science, 251(4995):767-773, 1991.
79. Fodor SP, Rava RP, Huang XC, Pease AC, Holmes CP, and Asams CL. Multiplexed biochemical assays with biological chips. Nature, 364:555-556, 1993.
80. Forgy E. Cluster analysis of multivariate data: efficency vs. interpretability of classification. Biometrics, 21:768, 1965.
81. Franklin RE and Gosling RG. Evidence for a 2-chain helix in the crystalline structure of sodium deoxyribonucleate. Nature, 172:156-157, 1953.
82. Franklin RE and Gosling RG. The structure of sodium thymonucleate fibres: I. The influence of water content. II. The cylindrically symmetrical Patterson function. Acta Crystallographica, 6:673-685, 1953.
83. Fraser CM, Gocayne JD, White O, Adams MD, Clayton RA, Fleischmann RD, Bult CJ, Kerlavage AR, Sutton G, Kelley JM, Fritchman RD, Weidman JF, Small KV, Sandusky M, Fuhrmann J, Nguyen D, Utterback TR, Saudek DM, Phillips CA, Merrick JM, Tomb JF, Dougherty BA, Bott KF, Hu PC, Lucier TS, Peterson SN, Smith HO, Hutchison CA III, and Venter JC. The minimal gene complement of Mycoplasma genitalium. Science, 270:397-403, 1995.
84. Fraser K, O’Neill P, Wang Z, and Liu X. Copasetic analysis: a framework for the blind analysis of microarray imagery. Systems Biology, 1(1):190-196, 2004.
85. Fraser K, O’Neill P, Wang Z, and Liu X. Copasetic analysis: automated analysis of biological gene expression images. International Conference on Control, Automation, Robotics and Vision, 1061-1066, December 6-9, 2004.
86. Fraser K, O’Neill P, Wang Z, and Liu X. Copasetic clustering of cDNA microarray imagery. In Mahammadian M. ed., International Conference on Computational Intelligence for Modelling Control and Automation, pages 779-787, July 12-14, 2004.
87. Fraser K, O’Neill P, Wang Z, and Liu X. Copasetic clustering: making sense of large-scale images. Lecture Notes in Computer Science, 3327:99-108, 2004.
88. Fraser K, Wang Z, Li Y, Kellam P, and Liu X. Improving microarray expressions with recalibration. In: Proc. International Symposium on Computational Life Science, 3-16, the Netherlands, October 2007.
89. Fraser K, Wang Z, Li Y, Kellam P, and Liu X. Noise filtering and microarray image reconstruction via chained fouriers. Lecture Notes in Computer Science, 4723:308-319, 2007.
90. Friedman N, Linial M, Nachman I, and Pe’er D. Using Bayesian networks to analyse expression data. In Proceedings ACM Research in Computational Molecular Biology (RECOMB), 127-135, Tokyo, Japan, 2000.
91. Frigui H and Krishnapuram RA. Robust competitive clustering algorithm with applications in computer vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(5):450-465, 1999.
92. Gasch AP, Spellman PT, Kao CM, Carmel-Harel O, Eisen MB, Storz G, Botstein D, and Bown PO. Genomic expression program in the response of yeast cells to environmental changes. Molecular Biology of the Cell, 11:4241-4257, 2000.
93. Geller CS, Gregg PJ, Hagerman P, and Rocke MD. Transformation and normalization of oligonucleotide microarray data. Bioinformatics, 19(14):1817-1823, 2003.
94. Gers F, Schraudolph N and Schmidhuber J. Learning precise timing with LSTM recurrent networks, Journal of Machine Learning Research, 3:115-143, 2002.
95. Ghahramani Z and Hinton GE. Parameter estimation for linear dynamical systems, Technical Report, University of Toronto, 1996.
96. Ghahramani Z. Learning Dynamic Bayesian Networks. In C.L. Giles and M. Gori (eds.), Adaptive Processing of Sequences and Data Structures. Lecture Notes in Artificial Intelligence, 168-197. Berlin: Springer-Verlag.
97. Giannakeas N, Fotiadis DI, and Politou AS. An automated method for gridding in microarray images. In Proceedings of 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 5876-5879, 2006.
98. Gonzalez RC and Woods RE. Digital Image Processing. New York: Prentice Hall, 2nd ed, 2002.
99. Hand DJ, Mannila H, and Smyth P. Principles of Data Mining, Cambridge, MA: MIT Press, 2001.
100. Hartelius K and Carstensen JM. Bayesian grid matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(2):162-173, 2003.
101. Hershey AD and Chase M. Independent functions of viral protein and nucleic acid in growth of bacteriophage. Journal General Physiology, 36:39-56, 1952.
102. Hillier LW, Miller W, Birney E, Warren W, Hardison RC, Ponting CP, Bork P, Burt DW, Groenen MA, Delany ME, Dodgson JB, Chinwalla AT, Cliften PF, Clifton SW, Delehaunty KD, Fronick C, Fulton RS, Graves TA, Kremitzki C, Layman D, Magrini V, McPherson JD, Miner TL, Minx P, Nash WE, Nhan MN, Nelson JO, Oddy LG, Pohl CS, Randall-Maher J, Smith SM, Wallis JW, Yang SP, Romanov MN, Rondelli CM, Paton B, Smith J, Morrice D, Daniels L, Tempest HG, Robertson L, Masabanda, JS, Griffin DK, Vignal A, Fillon V, Jacobbson L, Kerje S, Andersson L, Crooijmans RP, Aerts J, van der Poel JJ, Ellegren H, Caldwell RB, Hubbard SJ, Grafham DV, Kierzek AM, McLaren SR, Overton IM, Arakawa H, Beattie KJ, Bezzubov Y, Boardman PE, Bonfield JK, Croning MD, Davies RM, Francis MD, Humphray SJ, Scott CE, Taylor RG, Tickle C, Brown WR, Rogers J, Buerstedde JM, Wilson SA, Stubbs L, Ovcharenko I, Gordon L, Lucas S, Miller MM, Inoko H, Shiina T, Kaufman J, Salomonsen J, Skjoedt K, Wong GK, Wang J, Liu B, Wang J, Yu J, Yang H, Nefedov M, Koriabine M, Dejong PJ, Goodstadt L, Webber C, Dickens NJ, Letunic I, Suyama M, Torrents D, von Mering C, Zdobnov EM, Makova K, Nekrutenko A, Elnitski L, Eswara P, King DC, Yang S, Tyekucheva S, Radakrishnan A, Harris RS, Chiaromonte F, Taylor J, He J, Rijnkels M, Griffiths-Jones S, Ureta-Vidal A, Hoffman MM, Severin J, Searle SM, Law AS, Speed D, Waddington D, Cheng Z, Tuzun E, Eichler E, Bao Z, Flicek P, Shteynberg DD, Brent MR, Bye JM, Huckle EJ, Chatterji S, Dewey C, Pachter L, Kouranov A, Mourelatos Z, Hatzigeorgiou AG, Paterson AH, Ivarie R, Brandstrom M, Axelsson E, Backstrom N, Berlin S, Webster MT, Pourquie O, Reymond A, Ucla C, Antonarakis SE, Long M, Emerson JJ, Betran E, Dupanloup I, Kaessmann H, Hinrichs AS, Bejerano G, Furey TS, Harte RA, Raney B, Siepel A, Kent WJ, Haussler D, Eyras E, Castelo R, Abril JF, Castellano S, Camara F, Parra G, Guigo R, Bourque G, Tesler G, Pevzner PA, Smit A, Fulton LA, Mardis ER, and Wilson RK. International Chicken Genome Sequencing Consortium. 2004. Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature, 432:695-716, 2004.
103. Hirata R Jr, Barrera J, Hashimoto RF, and Dantas DO. Microarray gridding by mathematical morphology. In Proceedings of 14th Brazilian Symposium on Computer Graphics and Image Processing, 112-119, 2001.
104. Holloway AJ, Van Laar, Ryan K, Tothill RW, and Bowtell DL. Options available from start to finish for obtaining data from DNA microarrays II. Nature Genetics, 32:481-489, 2002.
105. Holter NS, Maritan A, Cieplak M, Fedoroff NV, and Banavar JR. Dynamic modeling of gene expression data, Proceedings of the National Academy of Science, USA, 98:1693-1698, 2001.
106. Huang S. Gene expression profiling, genetic networks, and cellular states: an integrating concept for tumorigenesis and drug discovery, Journal of Molecular Medicine, 77:469-480, 1999.
107. Hughes T, Marton M, Jones A, Roberts C, Stoughton R, Armour C, Bennett H, Coffey E, Dai H, and He Y. Functional discovery via a compendium of expression profiles. Cell, 102(1):109-126, 2000.
108. International Human Genome Sequencing Consortium. 2004. Finishing the euchromatic sequence of the human genome. Nature, 431:931-945, 2004.
109. Jain AK and Dubes RC. Algorithms For Clustering Data, 1st ed. Englewood Cliffs, NJ: Prentice Hall, 1988.
110. Jain AK, Murty MN, and Flynn PJ. Data clustering: a review. ACM Computing Surveys, 31(3):264-323, 1999.
111. Jain NA, Tokuyasu AT, Snijders MA, Segraves R, Albertson GD, and Pinkel D. Fully automated quantification of microarray image data. Genome Research, 12(2):325-332, 2002.
112. Jancey RC. Multidimensional group analysis. Australian Journal of Botany, 14:127-130, 1966.
113. Jansen R, Greenbaum D, and Gerstein M. Relating whole genome expression data with protein-protein interactions. Genome Research, 12:37-46, 2002.
114. Jung HY and Cho HG. An automatic block and spot indexing with k-nearest neighbors graph for microarray image analysis. Bioinformatics, 18(Suppl 2): S141-S151, 2002.
115. Katzer M, Kummbert F, and Sagerer G. A Markov random field model of microarray gridding. In Proceedings of the 18th ACM Symposium on Applied Computing, 72-77, New York: ACM Press, March, 2003.
116. Katzer M, Kummbert F, and Sagerer G. Methods for automatic microarray image segmentation. IEEE Transactions on Nanobioscience, 2(4):202-214, 2003.
117. Katzer M, Kummert F, and Sagerer G. Robust automatic microarray image analysis. In Proceedings International Conference on Bioinformatics: North-South Networking, Bangkok, Thailand, 2002.
118. Kaufmann L and Rousseeuw JP. Finding groups in data: an introduction to cluster analysis. Clustering Large Applications (Program CLARA), pages 126-163, New York: John Wiley & Sons, 1990.
119. Kellam P, Liu X, Martin N, Orengo CA, Swift S, and Tucker A. A framework for modelling virus gene expression data. Journal of Intelligent Data Analysis, 6(3):265-280, 2002.
120. Kepler BM, Crosby L, and Morgan TK. Normalization and analysis of DNA microarray data by self-consistency and local regression. Genome Biology, 3(7): research0037.1-research0037.12, 2002.
121. Kepler TB and Elston TC. Stochasticity in transcriptional regulation: origins, consequences, and mathematical representations, Biophys J, 81(6):3116-3136, 2001.
122. King B. Step-wise clustering procedures. Journal of the American Statistical Association, 69:86-101, 1967.
123. Kooperberg C, Fazzio TG, Delrow JJ, and Tsukiyama T. Improved background correction for spotted cDNA microarrays. Journal of Computational Biology, 9:55-66, 2002.
124. Kwatra V, Schödl A, Essa I, Turk G, and Bobick A. Graphcut Textures: image and video synthesis using graph cuts. In Proceedings of the 2003 ACM SIGGRAPH Conference, 277-286, San Diego, CA, July 2003.
125. Larsen B and Aone C. Fast and effective text mining using linear-time document clustering. In Chaudhuri S, Madigan D, and Fayyad U, editors, In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery, 16-22, New York: ACM Press, August, 15-18, 1999.
126. Lashkari DA, DeRisi JL, McCusker JH, Namath AF, Gentile C, Hwang SY, Brown PO, and Davis RW. Yeast microarrays for genome wide parallel genetic and gene analysis. In Proceedings National Academy of Sciences, 95:13057-13062, Washington, DC, 1997.
127. Lawrence ND, Milo M, Niranjan M, Rashbass P, and Soullier S. Reducing the variability in cDNA microarray image processing by Bayesian inference. Bioinformatics, 20(4):518-526, 2004.
128. Leach S and Hunter L. Comparative study of clustering techniques for gene expression microarray data. In Miyano S, Shamir R, and Akagi T, editors, Currents in Computational Molecular Biology, 1-2, Tokyo: University Academy Press, 2000.
129. Leavers VF. Shape Detection in Computer Vision Using the Hough Transform, Berlin: Springer, 1992.
130. Li QH, Fraley C, Bumgarner RE, Yeung KY, and Raftery AE. Donuts, scratches and blanks: Robust model-based segmentation of microarray images. Bioinformatics, 21(12):2875-2882, 2005.
131. Liang S, Fuhrman S and Somogyi R. REVEAL: A general reverse engineering algorithm for inference of genetic network architectures, Proc. of Pacific Symposium on Biocomputing, 3:18-29, 1998.
132. Liu T, Sung W and Mittal A. Model gene network by semi-fixed Bayesian network, Expert Systems with Applications, 30(1):42-49, 2006.
133. Liu X and Kellam P. Mining gene expression data. In Orengo CA, Jones DT, and Thornton JM, eds., Bioinformatics: Genes, Proteins and Computers, 1st ed. 229-244, Oxford: BIOS Scientific Publishers, 2003.
134. Ljung L. System Identification-Theory for the User, 2nd ed, PTR Upper Saddle River, N.J: Prentice Hall, 1999.
135. Lonardi S and Luo Y. Gridding and compression of microarray images. Proc IEEE Comput Syst Bioinform Conf., 122-130, 2004.
136. McAdams HM and Arkin A. Stochastic mechanisms in gene expression. Proceedings of the National Academy of Science, Washington, DC, 94:814-819, 1997.
137. Maduro MF and Rothman JH. Making worm guts: the gene regulatory network of the Caenorhabditis elegans endoderm, Dev. Biol., 246:68-85, 2002.
138. Matsuzaki M, Misumi O, Shin-I T, Maruyama S, Takahara M, Miyagishima SY, Mori T, Nishida K, Yagisawa F, Yoshida Y, Nishimura Y, Nakao S, Kobayashi T, Momoyama Y, Higashiyama T, Minoda A, Sano M, Nomoto H, Oishi K, Hayashi H, Ohta F, Nishizaka S, Haga S, Miura S, Morishita T, Kabeya Y, Terasawa K, Suzuki Y, Ishii Y, Asakawa S, Takano H, Ohta N, Kuroiwa H, Tanaka K, Shimizu N, Sugano S, Sato N, Nozaki H, Ogasawara N, Kohara Y, and Kuroiwa T. Genome sequence of the ultrasmall unicellular red alga Cyanidioschyzon merolae 10D. Nature, 428:653-657, 2004.
139. McQueen J. Some methods for classification and analysis of multivariate observations. In Le Cams L and Neyman S, eds., Proceedings of the 5th Berkeley Symposium on Mathematics Statistics and Probability, 281-297, 1967.
140. Mehnert A and Jackway P. An improved seeded region growing algorithm. Pattern Recognition Letters, 18:1065-1071, 1997.
141. Mitra S and Hayashi Y. Bioinformatics with soft computing. IEEE Transactions on Systems, Man and Cybernetics-Part C, 36(5):616-635, 2006.
142. Moore SK. Making chips. IEEE Spectrum, 38(3):54-60, 2001.
143. Motwani R and Raghavan P. Randomized Algorithms, Cambridge, UK and New York: Cambridge University Press, 1995.
144. Mouse Genome Sequencing Consortium. Initial sequencing and comparative analysis of the mouse genome. Nature, 420:520-562, 2002.
145. Mullis KB and Faloona FA. Specific synthesis of DNA in vitro via a polymerase-catalyzed chain reaction. Methods Enzymol, 155:335-350, 1987.
146. Murphy K and Mian S. Modelling gene expression data using dynamic Bayesian networks. Technical Report, Berkeley: University of California, 1999.
147. Murtagh F. A survey of recent advances in hierarchical clustering algorithms which use cluster centers. The Computer Journal, 26(4):354-359, 1983.
148. Murty MN and Krishna G. A computationally efficient technique for data clustering. Pattern Recognition, 12:153-158, 1980.
149. Nagarajan R and Peterson CA. Identifying genes in microarray images. IEEE Transactions on Nanobioscience, 1(2):78-84, 2002.
150. Netravali AN and Haskell BG. Digital pictures: representation, compression and standards, 2nd ed., New York: Plenum Press, 1995.
151. Ng R and Han J. Efficient and effective clustering method for spatial data mining. In Proceedings of the 20th International Conference on Very Large Data Bases, 144-155, September 1994.
152. Niblack W. An Introduction to Digital Image Processing, London: Prentice Hall international Ltd., 1986.
153. Nielsen F and Nock R, ClickRemoval: interactive pinpoint image object removal. In Proceedings of the 13th annual ACM international conference on Multimedia, 315-318, Hilton, Singapore, Nov. 2005.
154. Nirenberg MW and Matthaei HJ. The dependence of cell-free protein synthesis in E. coli upon naturally occurring or synthetic polyribonu-cleotides. In Proceedings National Academy of Sciences, 47:1589, Washington, DC, 1961.
155. Oliveira MM, Bowen B, McKenna R, and Chang YS. Fast digital image inpainting. In: Proceedings of the Visualization, Imaging and Image Processing, Marbella, Spain, September 2001, 261-266.
156. O’Neill P, Fraser K, Wang Z, Kellam P, Kok JN, and Liu X. Pyramidic clustering of large scale microarray imagery. The Computer Journal, 48:466-479, 2005.
157. O’Neill P, Magoulas GD, and Liu X. Improved processing of microarray data using image reconstruction techniques. IEEE Transactions on Nanobioscience, 2(4):176-183, 2003.
158. Otsu N. A threshold selection method from grey level histograms. IEEE Transactions on Systems, Man and Cybernetics, 8:62-66, 1978.
159. Pal SK, Bandyopadhyay S, and Ray SS, Evolutionary computation in bioinformatics: a review. IEEE Transactions on Systems, Man and Cybernetics-Part C, 36(5):601-615, 2006.
160. Pauling L and Corey RB. Two Hydrogen-bonded spiral configurations of the polypeptide chain. Journal American Chemical Society, 72:5349, 1950.
161. Pauling L and Corey RB. Stable configurations of polypeptide chains. In Proceedings Royal Society, 141:21-33, London, 1953.
162. Pauling L, Corey RB, and Branson HR. The structure of proteins, two hydrogen-bonded helical configurations of the polypeptide chain. In Proceedings National Academy Sciences, 37:205-511, Washington, DC, 1951.
163. Pease AC, Solas D, Sullivan J, Cronin MT, Holmes CP, and Fodor SP, Light-generated oligonucleotide arrays for rapid DNA sequence analysis. In Proceedings National Academy Sciences, 91(11):5022-5026, Washington, DC, 1994.
164. Perez P, Gangnet M, and Blake A. Poisson image editing. ACM Transactions on Graphics, 22(3):313-318, 2003.
165. Perkins WA. Area segmentation of images using edge points. IEEE Transactions on Pattern Recognition and Machine Intelligence, 2(1):8-15, 1980.
166. Petricoin EF, III, Emanuel F, Hackett JL, Lesko LJ, Puri RK, Gutman SI, Chumakov K, Woodcock J, Feigal JR, David W, Zoon CK, and Sistare DF. Medical applications of microarray technologies: a regulatory science perspective. Nature Genetics, 32:474-479, 2002.
167. Quackenbush J. Computational analysis of microarray analysis. Nature Reviews Genetics, 2(6):418-427, 2001.
168. Quackenbush J. Microarray data normalization and transformation. Nature Genetics, 32:490-495, 2002.
169. QuantArray. GSI Lumonics. http://www.bipl.ahc.umn.edu/quantarray.html, 2005.
170. Rahnenführer J and Bozinov D. Hybrid clustering for microarray image analysis combining intensity and shape features. BMC Bioinformatics, 5(47), 2004.
171. Ramoni MF, Sebastiani P, and Kohane IS. Cluster analysis of gene expression dynamics, Proceedings of the National Academy of Science, USA, 99:9121-9126, 2002.
172. Ranada S and Rosenfeld A. Point pattern matching by relaxation. Pattern Recognition, 12:269-275, 1980.
173. Rangel C, Angus J, Ghahramani Z, Lioumi M, Sotheran EA, Gaiba A, Wild DL, and Falciani F. Modeling T-cell activation using gene expression profiling and state space models, Bioinformatics, 20(9):1361-1372, 2004.
174. Rother C, Kolmogorov V, and Blake A. GrabCut-Interactive foreground extraction using iterated graph cuts. In Proceedings of the 2004 ACM SIGGRAPH conference, 309-314, Los Angeles, CA, August 2004.
175. Rueda L and Vidyadharan V. A hill-climbing approach for automatic gridding of cDNA microarray images. IEEE/ACM Trans Comput Biol Bioinform, 3(1):72-83.
176. Ruspini EH. A new approach to clustering. Information Control, 15(1):22-32, 1969.
177. Russell S and Norvig P. Artificial Intelligence A Modern Approach, Upper Saddle River, NJ: Prentice Hall, 2nd ed., 2003.
178. Samartzidou H, Turner L, Houts T, Frome J, Worley M, and Albertsen H. Lucidea microarray scorecard: an integrated analysis tool for microarray experiments, Life Science News, 7(13):1-10, 2001.
179. Sanger F, Nicklen S, and Coulson AR. DNA sequencing with chain-terminating inhibitors. In Proceedings National Academy Sciences, 74:5463-5467, 1977.
180. Sanger F, Air GM, Barrell BG, Brown NL, Coulson AR, Fiddes CA, Hutchison CA, Slocombe PM, and Smith M. Nucleotide sequence of bacteriophage phi X174 DNA. Nature, 165:687-695, 1977.
181. Sanger F, Coulson AR, Friedmann T, Air GM, Barrell BG, Brown NL, Fiddes JC, Hutchison CA 3rd, Slocombe PM, and Smith M. The nucleotide sequence of bacteriophage phi-X174. Journal Molecular. Biology, 125:225-246, 1977.
182. Sanger F, Coulson AR, Barrell BG, Smith AJ, and Roe BA. Cloning in single stranded bacteriophage as an aid to rapid DNA sequencing. Journal Molecular Biology, 143:161-178, 1980.
183. Scanalytics, Inc. MicroArray Suite, http://www.scanalytics.com/products/hts/microarray.html, 2005.
184. ScanAlyze Manual, http://rana.lbl.gov/manuals/ScanAlyzeDoc.pdf, Online Resource, 2007.
185. Schena M and Davis RW. In PCR Methods Manual, New York: Academic Press (in press).
186. Schena M, Shalon D, Davis RW, and Brown OP. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 210:467-470, 1995.
187. Schena D, Shalon R, Heller AC, Brown PO, and Davis RW. Parallel human genome analysis: microarray based expression monitoring of 1000 genes. In Proceedings National Academy Sciences, 93(20):10614-10619, USA, 1996.
188. Shumway RH and Stoffer DS. An approach to time series smoothing and forecasting using the EM algorithm, J. Time Series Anal., 3:253-264, 1982.
189. Smith LM. Fluorescence detection in automated DNA sequence analysis. Nature, 321:674-679, 1986.
190. Smolen P, Baxter DA and Byrne JH. Mathematical modeling of gene networks review, Neuron, 26(3):567-580, 2000.
191. Smyth KG, Yang HY, and Speed PT. Statistical issues in cDNA microarray data analysis. Functional Genomics: Methods and Protocols, 224:111-136, 2003.
192. Sneath PH and Sokal RR. Numerical Taxonomy – The Principles and Practice of Numerical Classification, San Francisco: W. H. Freeman, 1973.
193. Soille P. Morphological Image Analysis: Principles and Applications, New York: Springer, 1999.
194. Somogyi R and Sniegoski CA. Modeling the complexity of genetic networks: Understanding multigenic and pleiotropic regulation, Complexity, 1(6):45-63, 1996.
195. Southern EM. Detection of specific sequences among DNA fragments separated by gel electrophoresis. Journal Molecular Biology, 98:503-517, 1975.
196. Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PO, Botstein D, and Futcher B. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Molecular biology of the cell, 9(12):3273-3297, 1998.
197. Starck J, Murtagh FD, and Bijaoui A. Image Processing and Data Analysis: The Multiscale Approach, Cambridge, UK and New York: Cambridge University Press, 1998.
198. Steinbach M, Karypis G, and Kumar V. A comparison of document clustering techniques. In Proceedings of the 6th ACM SIGKDD World Text Mining Conference, 1-20, New York: ACM Press, August 20-23, 2000.
199. Steinbath M, Wruck W, Seidel H, Lehrach H, Radelof U, and O’Brien J. Automated image analysis for hybridization experiments. Bioinformatics, 17:634-641, 2001.
200. Sun J, Yuan L, Jia J, and Shum H. Image completion with structure propagation. In Proceedings of the 2005 ACM SIGGRAPH Conference, 861-868, New York: ACM Press, 2005.
201. Swift S and Liu X. Predicting glaucomatous visual field deterioration through short multivariate time series modelling, Artificial Intelligence in Medicine, 24:5-24, 2002.
202. Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES and Golub TR. Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation, Proceedings of the National Academy of Science, Washington, DC, 96:2907-2912, 1999.
203. Tavazoie S, Hughes JD, Campbell MJ, Cho RJ and Church GM. Systematic determination of genetic network architecture, Nature Genetics, 22(3):281-285, 1999.
204. Thattai T and van Oudenaarden A. Stochastic Gene Expression in Fluctuating Environments, Proc. of the Genetics Society of America, 523-530, 2004.
205. The Chipping Forcast I. Nature Genetics, 21(Supplement):1-60, 1999.
206. The Chipping Forcast II. Nature Genetics, 32:461-552, 2002.
207. Tian T and Burrage K. Stochastic neural network models for gene regulatory networks, Proc. of the 2003 IEEE Congress on Evolutionary Computation, 162-169, 2003.
208. Vincent L and Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13:583-598, 1991.
209. Wang XH, Istepanian RSH, and Song YH. Application of wavelet modulus maxima in microarray spots recognition. IEEE Transactions on Nanobioscience, 2(4):190-192, 2003.
210. Wang XH, Istepanian RSH, and Song YH. Microarray image enhancement by denoising using stationary wavelet transform. IEEE Transactions on Nanobioscience, 2(4):184-189, 2003.
211. Wang YK and Huang CW. DNA microarray image analysis using active contour model. In Proceedings of IEEE Computational Systems Bioinformatics Conference, 549-550, 2004.
212. Wann CD and Thomopoulos SA. A Comparative study of self-organizing clustering algorithms Dignet and ART2. Neural Networks, 10(4):737-743, 1997.
213. Ward JH. Hierarchical grouping to optimize an objective function. Journal of American Statistical Association, 58:236-244, 1963.
214. Watson JD. Involvement of RNA in the synthesis of proteins. Science, 140:17-26, 1963.
215. Watson JD and Crick FH. Genetic implications of the structure of deoxyribonucleic acid. Nature, 171:964-967, 1953.
216. Watson JD and Crick FH. Molecular structure of nucleic acids: a structure for deoxyribonucleic acid. Nature, 171:737-738, 1953.
217. Wang Z, Lam J, Wei G, Fraser K, and Liu X. Filtering for nonlinear genetic regulatory networks with stochastic disturbances. IEEE Transactions on Automatic Control, 53(10):2448-2457, 2008.
218. Wang Z, Gao H, Cao J, and Liu X. On delayed genetic regulatory networks with polytopic uncertainties: robust stability analysis. IEEE Transactions on NanoBioscience, 7(2):154-163, 2008.
219. Wang Z, Yang F, Ho DWC, Swift S, Tucker A, and Liu X. Stochastic dynamic modeling of short gene expression time series data. IEEE Transactions on NanoBioscience, 7(1):44-55, 2008.
220. Weinstein E, Oppenheim AV, Feder M, and Buck JR. Iterative and sequential algorithms for multisensor signal enhancement, IEEE Trans. Signal Processing, 42:846-859, 1994.
221. Wessels LF, Someren E Pvan, and Reinders MJ. A comparison of genetic network models, Proc. of Pacific Symposium on Biocomputing, 508-519, 2001.
222. Wu F, Zhang W, and Kusalik AJ. Modeling gene expression from microarray expression data with state-space equations, Proc. of Pacific Symposium on Biocomputing, 581-592. Hawaii, USA, 2004.
223. Xia Q, Zhou Z, Lu C, Cheng D, Dai F, Li B, Zhao P, Zha X, Cheng T, Chai C, Pan G, Xu J, Liu C, Lin Y, Qian J, Hou Y, Wu Z, Li G, Pan M, Li C, Shen Y, Lan X, Yuan L, Li T, Xu H, Yang G, Wan Y, Zhu Y, Yu M, Shen W, Wu D, Xiang Z, Yu J, Wang J, Li R, Shi J, Li H, Li G, Su J, Wang X, Li G, Zhang Z, Wu Q, Li J, Zhang Q, Wei N, Xu J, Sun H, Dong L, Liu D, Zhao S, Zhao X, Meng Q, Lan F, Huang X, Li Y, Fang L, Li C, Li D, Sun Y, Zhang Z, Yang Z, Huang Y, Xi Y, Qi Q, He D, Huang H, Zhang X, Wang Z, Li W, Cao Y, Yu Y, Yu H, Li J, Ye J, Chen H, Zhou Y, Liu B, Wang J, Ye J, Ji H, Li S, Ni P, Zhang J, Zhang Y, Zheng H, Mao B, Wang W, Ye C, Li S, Wang J, Wong GK, and Yang H. A draft sequence for the genome of the domesticated silkworm (Bombyx mori). Science, 306:1937-1940, 2004.
224. Xu C and Prince JL. Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing, 7(3):359-369, 1998.
225. Xu R and Wunsch D. Survey of clustering algorithms. IEEE Transactions on Neural Networks, 16(3):645-678, 2005.
226. Yang HY, Dudoit S, Luu P, and Speed PT. Normalisation for cDNA Microarray Data, 2000.
227. Yang YH, Buckley MJ, Dudoit S, and Speed TP. Comparison of methods for image analysis on cDNA microarray data. Journal of Computational and Graphical Statistics, 11:108-136, 2002.
228. Yeung KY, Fraley C, Murua A, Raftery AE, and Ruzzo WL. Model-based clustering and data transformations for gene expression data, Bioinformatics, 17(10):977-987, 2001.
229. Zadeh LA. Fuzzy sets. Information Control, 8(1):338-353, 1965.
230. Zhang T, Ramakrishnan R, and Livny M. BIRCH: An efficient data clustering method for very large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data, 103-114, New York: ACM Press, June, 1996.
231. Ziskind I and Hertz D. Maximum-likelihood localization of narrow-band autoregressive sources via the EM algorithm, IEEE Trans. Signal Processing, 41(8):2719-2724, 1993.