Title:
BAYESIAN NETWORK FRAMEWORKS FOR BIOMEDICAL DATA MINING
Document Type and Number:
WIPO Patent Application WO2005119582
Kind Code:
A3
Abstract:
A system (100) and method (200) for data classification are provided, the system including a processor (102), an adapter (112) in signal communication with the processor for receiving data, a filtering unit (170) in signal communication with the processor for pre-processing the data and filtering features of the data, a selection unit (180) in signal communication with the processor for learning a Bayesian network (BN) classifier and selecting features responsive to the BN classifier, and an evaluation unit (190) in signal communication with the processor for evaluating a model responsive to the BN classifier; and the method including receiving data (212), pre-processing the data (214), filtering features of the data (216), learning a BN classifier (218), selecting features responsive to the BN classifier (220), and evaluating a model responsive to the BN classifier (222).
Inventors:
CHENG JIE (US)
NEUBAUER CLAUS (US)
NEUBAUER CLAUS (US)
Application Number:
PCT/US2005/014718
Publication Date:
May 04, 2006
Filing Date:
May 02, 2005
Export Citation:
Assignee:
SIEMENS CORP RES INC (US)
CHENG JIE (US)
NEUBAUER CLAUS (US)
CHENG JIE (US)
NEUBAUER CLAUS (US)
International Classes:
G06K9/62
Other References:
J. CHENG ET AL.: "KDD Cup 2001 Report", SIGKDD EXPLORATIONS, vol. 3, no. 2, January 2002 (2002-01-01), XP002370957
JIE CHENG: "J Cheng's Bayesian Belief Network Software", WEBARCHIVE.ORG, February 2003 (2003-02-01), XP002370958, Retrieved from the Internet [retrieved on 20060306]
CHENG J., BELL D. AND LIU W.: "Learning Bayesian Networks from Data: An Efficient Approach Based on Information Theory", 1998, XP002370932, Retrieved from the Internet [retrieved on 20060307]
FRIEDMAN, N., LINIAL, M., NACHMAN, I., AND PE'ER, D.: "Using bayesian network to analyze expression data", JOURNAL OF COMPUTATIONAL BIOLOGY, vol. 7, 2000, pages 601 - 620, XP002370937
P. HELMAN, R. VEROFF, S. ATLAS AND C. WILLMAN: "A Bayesian Network Classification Methodology for Gene Expression Data", JOURNAL OF COMPUTATIONAL BIOLOGY, vol. 11, no. 4, August 2004 (2004-08-01), pages 581 - 615, XP002370934
HUSMEIER D.: "Sensitivity and specificity of inferring genetic regulatory interactions from microarra experiments with dynamic Bayesian networks", BIOINFORMATICS, vol. 19, no. 17, 2003, pages 2271 - 2282, XP002370933
CATALTEPE Z. ET AL.: "Using ROC curve in the absence of positive examples", SIXTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND NATURAL COMPUTING, 26 September 2003 (2003-09-26), Cary, North Carolina, USA, XP002370935
FRIEDMAN N. AND KOLLER D.: "Being Bayesian About Network Structure. A bayesian Approach to Structure Discovery in Bayesian Networks", MACHINE LEARNING, no. 50, 2003, pages 95 - 125, XP002370936
JIE CHENG: "J Cheng's Bayesian Belief Network Software", WEBARCHIVE.ORG, February 2003 (2003-02-01), XP002370958, Retrieved from the Internet
CHENG J., BELL D. AND LIU W.: "Learning Bayesian Networks from Data: An Efficient Approach Based on Information Theory", 1998, XP002370932, Retrieved from the Internet
FRIEDMAN, N., LINIAL, M., NACHMAN, I., AND PE'ER, D.: "Using bayesian network to analyze expression data", JOURNAL OF COMPUTATIONAL BIOLOGY, vol. 7, 2000, pages 601 - 620, XP002370937
P. HELMAN, R. VEROFF, S. ATLAS AND C. WILLMAN: "A Bayesian Network Classification Methodology for Gene Expression Data", JOURNAL OF COMPUTATIONAL BIOLOGY, vol. 11, no. 4, August 2004 (2004-08-01), pages 581 - 615, XP002370934
HUSMEIER D.: "Sensitivity and specificity of inferring genetic regulatory interactions from microarra experiments with dynamic Bayesian networks", BIOINFORMATICS, vol. 19, no. 17, 2003, pages 2271 - 2282, XP002370933
CATALTEPE Z. ET AL.: "Using ROC curve in the absence of positive examples", SIXTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND NATURAL COMPUTING, 26 September 2003 (2003-09-26), Cary, North Carolina, USA, XP002370935
FRIEDMAN N. AND KOLLER D.: "Being Bayesian About Network Structure. A bayesian Approach to Structure Discovery in Bayesian Networks", MACHINE LEARNING, no. 50, 2003, pages 95 - 125, XP002370936
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