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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 1-2/2005

01.09.2005 | Theoretical Advances

A supervised data-driven approach for microarray spot quality classification

verfasst von: Manuele Bicego, Maria Del Rosario Martinez, Vittorio Murino

Erschienen in: Pattern Analysis and Applications | Ausgabe 1-2/2005

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Abstract

In this paper, the problem of classifying the quality of microarray data spots is addressed, using concepts derived from the supervised learning theory. The proposed method, after extracting spots from the microarray image, computes several features, which take into account shape, color and variability. The features are classified using support vector machines, a recent statistical classification technique that is being employed widely. The proposed method does not make any assumptions on the problem and does not require any a priori information. The proposed system has been tested in a real case, for several different parameters’ configurations. Experimental results show the effectiveness of the proposed approach, also in comparison with state-of-the-art methods.
Vorheriger Artikel Use of colour for hand-filled form analysis and recognition
Nächster Artikel Merging face and finger images for human identification

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Fußnoten
1
The B-Course method used by the authors to infer the structure of the Naive Bayesian Networks merely represents a feature selection step.
 
2
The bleeding could be defined as the phenomenon in which a spot spreads so much that it is mixed with its neighbors should be carefully avoided.
 
3
In classification, only the sign is used, not the magnitude.
 
4
Data, together with experiments’ descriptions, data specifications, figures, experts’ classifications and labels are available on the web site http://​sigwww.​cs.​tut.​fi/​TICSP/​SpotQuality/​.
 
Literatur
1.
Model F, König T, Piepenbrock C, Adorján P (2002) Statistical process control for large scale microarray experiments. Bioinformatics 18:155S–163S
2.
Brändle N, Bischof H, Lapp H (2003) Robust dna microarray image analysis. Machine Vision and Applications 15:11 – 28CrossRef
3.
Valafar F (2002) Pattern recognition techniques in microarray data analysis: A survey. Ann N Y Acad Sci, Special issue on Tech Bioinform Med Inform 980:41–64
4.
Mukherjee S (2003) Classifying microarray data using support vector machines. In: Berrar D, Dubitzky W, Granzow M (eds) A Practical Approach to Microarray Data Analysis. Kluwer, pp 166–185
5.
Wang X, Ghosh S, Guo S (2001) Quantitative quality control in microarray image processing and data acquisition. Nucleic Acids Res 29:75CrossRefPubMed
6.
Chen Y, Kamat V, Dougherty E, Bittner M, Meltzer P, Trent J (2002) Ratio statistics of gene expression levels and applications to microarray data analysis. Bioinformatics 18:1207–1215CrossRefPubMed
7.
Brown C, Goodwin P, Sorger P (2001) Image metrics in the statistical analysis of DNA microarray data. Proc Natl Acad Sci USA 98:8944–8949CrossRefPubMed
8.
Buhler J, Ideker T, Haynor D (2000) Dapple: improved techniques for finding spots on DNA microarrays. Technical Report UWTR 2000-08-05, Department of Computer Science and Engineering, University of Washington
9.
Hautaniemi S, Edgren H, Vesanen P, Wolf M, Järvinen A, Yli-Harja O, Astola J, Kallioniemi O, Monni O (2003) A novel strategy for microarray quality control using Bayesian networks. Bioinformatics 19:2031–2038CrossRefPubMed
10.
11.
Vapnik V (1995) The nature of statistical learning theory. Springer, Berlin Heidelberg New York
12.
Burges C (1998) A tutorial on support vector machine for pattern recognition. Data Min Knowl Disc 2:121–167CrossRef
13.
Jollife I (1986) Principal component analysis. Springer, Berlin Heidelberg New York
14.
Jonsson K, Kittler J, Li YP, Matas J (1993) Support vector machines for face authentication. In: Proceeding of Brit Machine Vision Conf, Nottingham, UK pp 543–553
15.
Bicego M, Iacono G, Murino V (2003) Face recognition with multilevel B-splines and support vector machines. In: Proceeding of ACM SIGMM multimedia biometrics methods and applications workshop, pp 17–24
16.
Pontil M, Verri A (1998) Support vector machines for 3-D object recognition. IEEE T Pattern Anal Mach Intell 20:637–646CrossRef
17.
Murino V, Bicego M, Rossi I (2004) Statistical classification of raw textile defects. In: Proceedings of IEEE International conference on pattern recognition 4:311–314
18.
Castellani A, Botturi D, Bicego M, Fiorini P (2004) Hybrid HMM/SVM model for the analysis and segmentation of teleoperation tasks. In: Proceeding of IEEE International conference on robotics and automation 3:2918–2923
19.
Platt JC (1998) Fast training of support vector machines using sequential minimal optimization. Advances in Kernel methods-support vector learning
20.
Hartelius K, Carstensen J (2003) Bayesian grid matching. IEEE T Pattern Anal Mach Intell 25:162–173CrossRef
21.
Draper N, Smith H (1998) Applied regression analysis, 3rd edn. Wiley, New York
22.
Duda R, Hart P, Stork D (2001) Pattern Classification, 2nd edn. Wiley, New York
23.
Bicego M, Grosso E, Tistarelli M (2005) Probabilistic face authentication using hidden markov models. In: Proc of SPIE International Workshop on Biometric Technology for Human Identification
24.
Gunn S (1997) Support vector machines for classification and regression. Technical report, Image Speech and Intelligent Systems Research Group, University of Southampton
25.
Vapnik V (1998) Statistical Learning Theory. John Wiley, New York
26.
Mallat S, Zhang Z (1993) Matching pursuit with time-frequency dictionaries. IEEE T Signal Proces 41:3397–3415CrossRef
Metadaten
Titel
A supervised data-driven approach for microarray spot quality classification
verfasst von
Manuele Bicego
Maria Del Rosario Martinez
Vittorio Murino
Publikationsdatum
01.09.2005
Erschienen in
Pattern Analysis and Applications / Ausgabe 1-2/2005
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-005-0254-5

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