Kernels and kernel methods for spectral data

Details Kernels and kernel methods for spectral data Kernels and kernel methods for spectral data

2002-10-09

2009-11-10

Vincent, David R (Department: 2129)

Data processing: artificial intelligence

Machine learning

C706S045000, C706S020000, C706S025000

Reexamination Certificate

active

07617163

ABSTRACT:
Support vector machines are used to classify data contained within a structured dataset such as a plurality of signals generated by a spectral analyzer. The signals are pre-processed to ensure alignment of peaks across the spectra. Similarity measures are constructed to provide a basis for comparison of pairs of samples of the signal. A support vector machine is trained to discriminate between different classes of the samples. to identify the most predictive features within the spectra. In a preferred embodiment feature selection is performed to reduce the number of features that must be considered.

REFERENCES:
patent: 5143854 (1992-09-01), Pirrung et al.
patent: 5649068 (1997-07-01), Boser et al.
patent: 5837832 (1998-11-01), Chee et al.
patent: 5950146 (1999-09-01), Vapnik
patent: 6128608 (2000-10-01), Barnhill
patent: 6157921 (2000-12-01), Barnhill
patent: 6161130 (2000-12-01), Horvitz et al.
patent: 6267722 (2001-07-01), Anderson et al.
patent: 6427141 (2002-07-01), Barnhill
patent: 6658395 (2003-12-01), Barnhill
patent: 6675104 (2004-01-01), Paulse et al.
patent: 6714925 (2004-03-01), Barnhill et al.
patent: 6760715 (2004-07-01), Barnhill et al.
patent: 6789069 (2004-09-01), Barnhill et al.
patent: 6882990 (2005-04-01), Barnhill et al.
patent: 6990217 (2006-01-01), Moghaddam et al.
patent: 2002/0059151 (2002-05-01), Soufian et al.
patent: 2003/0040904 (2003-02-01), Whitman et al.
patent: 2003/0078739 (2003-04-01), Norton et al.
patent: 2003/0111596 (2003-06-01), Becker et al.
patent: 2003/0172043 (2003-09-01), Guyon et al.
patent: 2004/0048278 (2004-03-01), Olek et al.
patent: 2004/0142334 (2004-07-01), Schacht
patent: 2005/0071300 (2005-03-01), Bartlett et al.
patent: 2005/0100992 (2005-05-01), Noble
patent: 2005/0116159 (2005-06-01), Becker et al.
patent: 2005/0216426 (2005-09-01), Weston et al.
patent: 2006/0064415 (2006-03-01), Guyon et al.
Leslie et al, “The Spectrum Kernel: A String Kernel for SVM Protein Classification”, Pacific Symposium on Computing, Jan. 3-7, 2002.
Zhang et al, “Classification of Hyperspectral Data Using Support Vector Machine”, IEEE, 2001.
Altschul et al., Basic Local Alignment Search Tool, 1990.
Baldi et al., Hidden Markov models of biological primary sequence information, 1994.
Schouten et al., A Neural Network Approach to Spectral Mixture Analysis, 1997.
Brown et al (“Knowledge-based analysis of microarray gene expression data by using support vector machines” Jan. 2000.
Berndt et al (“Reliable automatic protein identification from matrix-assisted laser desorption/ionization mass spectrometric peptide fingerprints” 1999.
Duan, K., et al., “SVM-RFE Peak Selection for Cancer Classification with Mass Spectrometry Data”, pp. 191-200, Proc. 3rdAsia-Pacific Bioinformatics Conf., Jan. 17-21, 2005.
Hastings, C.A., et al., “New algorithms for processing and peak detection in liquid chromatography/mass spectrometry data”,Rapid Commun. Mass Spectrom.2002;16(5):462-7 (abstract).
Levner, I., “Feature selection and nearest centroid classification for protein mass spectrometry”,BMC Bioinformatics, Mar. 23, 2005, 6:68.
Norton, S.M., et al., “Data mining of spectroscopic data for biomarker discovery”,Curr Opin Drug Discov Devel.May 2001;4(3):325-31.
Wagner, Michael, et al., “Computational protein biomarker prediction: a case study for prostate cancer”,BMC Bioinformatics, Mar. 11, 2004, 5:26.
Yu, Jie-Kai, et al., “An integrated approach to the detection of colorectal cancer utilizing proteomics and bioinformatics”,World Journal of Gastroenterology, Nov. 1, 2004, pp. 3127-3131, 10(21).
Aerts, “Chitotriosidase—New Biochemical Marker”,Gauchers News, Mar. 1996.
Alizadeh, et al., “Distinct Types of Diffuse Large B-cell Lymphoma Identified by Gene Expression Profiling”,Nature, Feb. 2000, 503-511, vol. 403.
Alon, et al., “Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Cancer Tissues Probed by Oligonucleotide Arrays”,Proc. Natl. Acad. Sci., USA, Jun. 1999, pp. 6745-6750, vol. 96, Cell Biology.
Aronson, “Remodeling the Mammary Gland at the Termination of Breast Feeding: Role of a New Regulator Protein BRP39”,The Beat, University of South Alabama College of Medicine, Jul. 1999.
Barash, et al., “Context-Specific Bayesian Clustering for Gene Expression Data”,Proceedings of the 5thAnnual International Conference on Computational Biology, Apr. 2001, pp. 12-21, (Abstract).
Ben-Dor et al., “Tissue Classification with Gene Expression Profiles”,Proceedings of the 4thAnnual International Conference on Computational Molecular Biology, Apr. 2000, pp. 54-64, (Abstract).
Boser, et al., “A Training Algorithm for Optimal Margin Classifiers”,Fifth Annual Workshop on Computational Learning Theory, 1992, pp. 144-152, ACM, Pittsburgh.
Breiman, “Bagging Predictors”,Machine Learning, Aug. 1996, pp. 123-140, vol. 24, Issue 2, (Technical Report No. 421, Sep. 1994, University of California, Berkeley, CA, USA).
Brown, et al., “Knowledge-Based Analysis of Microarray Gene Expression Data by Using Support Vector Machines”,Proceedings of the National Academy of Sciences of USA, Jan. 4, 2000, pp. 262-267, vol. 97, No. 1, Nation Academy of Science, Washington, US.
Burges, C. J. C., “Geometry and Invariance in Kernel Based Methods”,Advances in Kernel Methods—Support Vector Learning, Edited by Schölkopf, Burges, and Smola, 1999, MIT Press.
Chapelle, O., et al., “Choosing Multiple Parameters for Support Vector Machines”,Machine Learning, 2002, pp. 131-159, vol. 46, No. 1.
Cun, et al., “Optimal Brain Damage”,Advances in Neural Information Processing Systems II, 1990.
Decoste, D., et al., M. C., “Distortion-Invariant Recognition Via Jittered Queries.”,Computer Vision and Pattern Recognition(CVPR-2000), Jun. 2000.
Decoste, D., et al., “Training Invariant Support Vector Machines”,Machine Learning, 2002, vol. 46, No. 3.
Devijver, et al., “Pattern Recognition. A Statistical Approach”,Introduction to feature selection and Extraction, pp. 218-219, Sections: Sequential Backword Selection (SBS) and Generalized Sequential Backward Selection (GSBS), Prentice-Hall International, London.
Dietterich, “Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms”,Neural Computation, 1998, pp. 1895-1923, vol. 10, Issue 7, MIT Press.
Eisen, et al., “Cluster Analysis and Display of Genome-wide Expression patterns”,Genetics, Dec. 1998, pp. 14863-14868, vol. 95, Proc. Natl. Acad. Sci.
Freund, et al., “A Short Introduction to Boosting”,Journal of Japanese Society for Artificial Intelligence, 1999, pp. 771-780, vol. 14, No. 5.
Ghina, et al., “Altered Expression of Heterogeneous Nuclear Ribonucleoproteins and SR Factors in Human”,Cancer Research, Dec. 15, 1998, pp. 5818-5824, vol. 58, (Abstract).
Golub, et al., “Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring”,Science, pp. 531-537, Oct. 15, 1999, vol. 286.
Guyon, et al., “Discovering Informative Patterns and Data Cleaning”,Advances in Knowledge Discovery and Data Mining, 1996, pp. 181-203, MIT Press, (Abstract).
Guyon, et al., “Gene Selection for Cancer Classification Using Support Vector Machines”,Machine Learning, 2002, pp. 389-422, vol. 46.
Guyon, et al., “Structural risk Minimization for Character Recognition”,Advances In Neural Information Processing Systems, 1991, pp. 471-479, vol. 4, Morgan Kaufmann, San Mateo, CA.
Guyon, et al., “What Size Test Set Gives Good Error Rate Estimates”,IEEE, 1998, pp. 52-64, vol. 20, No. 1, (Abstract).
Harlan, et al., “The Human Myristoylated Alanine-rich C Kinase Substrate (MARCKS) Gene (MACS). Analysis of its Gene Pro

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