Joint classification and subtype discovery in tumor...

Image analysis – Pattern recognition – Classification

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C382S133000, C382S156000, C382S159000, C702S020000, C706S019000, C706S020000, C706S045000

Reexamination Certificate

active

07664328

ABSTRACT:
A program storage device is provided readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for classification of biological tissue by gene expression profiling. The method steps include providing a training set of gene expression profiles of known tissue samples, providing a first-layer strong classifier of the known tissue samples by combining weak classifiers using boosting, creating two sample sets based on the first classifier, populating the two sample sets with a next-layer of classifiers based on a previous-layer classifier, organizing the classifiers in a tree data structure, and outputting the tree data structure as a probabilistic boosting tree classifier for tissue sample classification and disease subtype discovery. A multi-class diagnosis problem is transformed to a two-class diagnosis process by finding an optimal feature and dividing the multi-class problem into two-classes.

REFERENCES:
patent: 7117188 (2006-10-01), Guyon et al.
patent: 7370021 (2008-05-01), Reeve et al.
patent: 7428554 (2008-09-01), Coberley et al.
patent: 7593913 (2009-09-01), Wang et al.
patent: 2003/0172043 (2003-09-01), Guyon et al.
patent: 2003/0225526 (2003-12-01), Golub et al.
patent: 2003/0233197 (2003-12-01), Padilla et al.
patent: 2004/0236723 (2004-11-01), Reymond
patent: 2005/0069863 (2005-03-01), Moraleda et al.
patent: 2006/0074834 (2006-04-01), Dong et al.
patent: 2007/0071313 (2007-03-01), Zhou et al.
patent: 2008/0027917 (2008-01-01), Mukherjee et al.
Freund, Y. and Schapire, RE (1999) “A Short Introduction to Boosting,” Journal of Japanese Society for Artificial Intelligence, 14(5), 771-780.
Shapiro, RE (2002) “The Boosting Approach to Machine Learning an overview” MSRI Workshop on Nonlinear Estimation and Classification, Lecture Notes in Computer Science, Spring-Verlag.
Viola, P. et al., (2001) “Rapid Object Detection Using A Boosted Cascade of Simple Features,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cvpr) 1,511-518.
Alexandridis, R., Lin, S. and Irwin, M. (2004) “Class Discovery and Classification of Tumor Samples Using Mixture Modelling of Gene Expression Data,” Bioinformatics, Advanced Access, 20:16 2545-2552.
Alon, U. et al., (1999) “Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Tissues Probed by Oligonucleotide Arrays,” Proc Natl. Acad. Sci USA 96,6745-6750.
Ben-Dor. A. et al., (2000) “Tissue Classification With Gene Expression Profiles,” Journal of Computational Biology, 7 559-584.
Brown et al. (2000) “Knowledge-Based Analysis of Microarray Gene Expression Data by Using Support Vector Machines,” Proceeding of National Academy of Science 97:262-267.
Deftling, M. et al, (2003) “Boosting for Tumor Classification With Gene Expression Data,” Bioinformatics 19:9 1061-1069.
Dudoit, S. et al., (2000) “Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data,” Technical Report 576, Department of Statistics, University of California, Berkely.
Friedman, N. et al. (2000) “Using Bayesian Network to Analyze Expression Data,” Journal of Computational Biology 7:601-620.
Furey, T. S. et al., (2000) “Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data,” Bioinformatics 16:10 906-914.
Golub, T. R. et al., (2002) “Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring,” Science 286:531-537.
Liu, Y. et al. (2004) “Multiclass Discovery in Array Data,” Bmcbioinformatics, 5 70.
Nguyen, D. et al. (2002) “Tumor Classification by Partial Least Squares Using Microarray Gene Expression Data,” Bioinformatics, 18:1 39-50.
Ramaswany, S. et al. (2001) “Multiclass Cancer Diagnosis Using Tumor Gene Expression Signatures,” Proc Natl. Acad. Sci. USA, 98 15149-15154.
Varma S., et al. “Iterative Class Discovery and Feature Selection Using Minimal Spanning Trees,” Bmc Bioinformatics, 5 126.

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Joint classification and subtype discovery in tumor... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Joint classification and subtype discovery in tumor..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Joint classification and subtype discovery in tumor... will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-4205997

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.