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Application Of Machine Learning Methods For Mining Association Rules In Plant And Animal Data Sets Containing Molecular Genetic Markers, Followed By Classification Or Prediction Utilizing Features Created From These Association Rules - EP2449510

The patent EP2449510 was granted to DOW Agrosciences on Dec 21, 2022. The application was originally filed on Jun 3, 2010 under application number EP10728031A. The patent is currently recorded with a legal status of "Patent Maintained As Amended".

EP2449510

DOW AGROSCIENCES
Application Number
EP10728031A
Filing Date
Jun 3, 2010
Status
Patent Maintained As Amended
Nov 18, 2022
Grant Date
Dec 21, 2022
External Links
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Patent Citations (1) New

Patent citations refer to prior patents cited during different phases such as opposition or international search.

Citation PhasePublication NumberPublication Link
INTERNATIONAL-SEARCH-REPORTWO02080079

Non-Patent Literature (NPL) Citations (45) New

NPL citations refer to non-patent references such as research papers, articles, or other publications cited during examination or opposition phases.

Citation PhaseReference TextLink
DESCRIPTION- BOSER ET AL., A TRAINING ALGORITHM FOR OPTIMAL MARGIN CLASSIFIERS V, (1992), pages 144 - 52-
DESCRIPTION- CHENG, "Direct Discriminative Pattern Mining for Effective Classification", ICDE, (2008), pages 169 - 178, XP031245974-
DESCRIPTION- CHENG, "Discriminative Frequent Pattern Analysis for Effective Classification", ICDE, (2007), pages 716 - 725, XP031095815-
DESCRIPTION- CHENG; GREINER, "Comparing Bayesian network classifiers", PROCEEDINGS UAI, (1999), pages 101 - 107-
DESCRIPTION- DOMINGOS, PEDRO; MICHAEL PAZZANI, "On the optimality of the simple Bayesian classifier under zero-one loss", MACHINE LEARNING, (1997), vol. 29, pages 103 - 137-
DESCRIPTION- FREUND; MASON, "The alternating decision tree learning algorithm", PROC. SIXTEENTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING, (1999), pages 124 - 133-
DESCRIPTION- FRIEDMAN ET AL., "Bayesian network classifiers", MACHINE LEARNING, (1997), vol. 29, pages 131 - 163-
DESCRIPTION- GUYON; ELISSEEFF, "An Introduction to Variable and Feature Selection", JOURNAL OF MACHINE LEARNING RESEARCH, (2003), vol. 3, pages 1157 - 1182-
DESCRIPTION- HONG, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, (1997), vol. 9, no. 5, pages 718 - 730-
DESCRIPTION- JIYONG, "HARMONY: Efficiently Mining the Best Rules for Classification", SIAM, (2005), pages 205 - 216-
DESCRIPTION- KONONENKO, "On biases in estimating multi-valued attributes", IJCAI, (1995), vol. 95, pages 1034 - 1040-
DESCRIPTION- PAN, "CARPENTER: Finding Closed Patterns in Long Biological Datasets", ACM SIGKDD, (2003), pages 637 - 642-
DESCRIPTION- ROBNIK-SIKONJA; KONONENKO, "Theoretical and empirical analysis of Relief and ReliefF", MACHINE LEARNING, (2003), vol. 53, pages 23 - 69-
DESCRIPTION- SCHMITT, LOTHAR M, THEORY OF GENETIC ALGORITHMS, THEORETICAL COMPUTER SCIENCE, (2001), pages 1 - 61-
DESCRIPTION- SCHOLKOPF, "SVMs - a practical consequence of learning theory", IEEE INTELLIGENT SYSTEMS. IEEE INTELLIGENT SYSTEMS, (1998), vol. 13.4, pages 18 - 21-
DESCRIPTION- SMOLA; SCHOLKOPF, "A Tutorial on Support Vector Regression", NEUROCOLT2 TECHNICAL REPORT SERIES - NC2-TR, (1998), pages 1998 - 030-
DESCRIPTION- WANG, "CLOSET+: Searching for best strategies for mining frequent closed itemsets", ACM SIGKDD, (2003), pages 236 - 245-
DESCRIPTION- WANG; WITTEN, "Inducing Model Trees for Continuous Classes", 9TH EUROPEAN CONFERENCE ON MACHINE LEARNING, (1997), pages 128 - 137-
DESCRIPTION- ZAKI, "CHARM: An efficient algorithm for closed itemset mining", SIAM, (2002), pages 457 - 473-
DESCRIPTION- ZHU, "Mining Colossal Frequent Patterns by Core Pattern Fusion", ICDE, (2007), pages 706 - 715, XP031095814-
DESCRIPTION- KIANMEHR ET AL., "CARSVM: A class association rule-based classification framework and its application in gene expression data", ARTIFICIAL INTELLIGENCE IN MEDICINE, (2008), doi:doi:10.1016/j.artmed.2008.05.002, pages 7 - 25, XP024340295
DESCRIPTION- KOHAVI; JOHN, "Wrappers for feature selection", ARTIFICIAL INTELLIGENCE, (1997), vol. 97, no. 1-2, doi:doi:10.1016/S0004-3702(97)00043-X, pages 273 - 324, XP002263359
DESCRIPTION- BURGES, "A tutorial on support vector machines for pattern recognition", DATA MINING AND KNOWLEDGE DISCOVERY, (1998), vol. 2, doi:doi:10.1023/A:1009715923555, pages 121 - 67, XP002087854
DESCRIPTION- PAN ET AL., "COBBLER: Combining Column and Row Enumeration for Closed Pattern Discovery", SSDBM, (2004), doi:doi:10.1109/SSDM.2004.1311190, page 21, XP010709022
DESCRIPTION- CONG, "Mining top-K covering rule groups for gene expression data", ACM SIGMOD, (2005), doi:doi:10.1145/1066157.1066234, pages 670 - 681, XP058158609
INTERNATIONAL-SEARCH-REPORT- "Finding frequent sets and association rules", Hand, Mannila, Smyth, Principles of Data Mining, US, Cambridge, MIT Press, (2006), 13.3.2, ISBN 978-0-262-08290-7, XP002600773 [A] 1-57-
INTERNATIONAL-SEARCH-REPORT- LONG A D ET AL, "The Power of Association Studies to Detect the Contribution of Candidate Genetic Loci to Variation in Complex Traits", GENOME RESEARCH, COLD SPRING HARBOR LABORATORY PRESS, WOODBURY, NY, US, (19990801), vol. 9, ISSN 1088-9051, pages 720 - 731, XP002222375 [I] 1-57 * the whole document *-
INTERNATIONAL-SEARCH-REPORT- AGRAWAL R ET AL, "MINING ASSOCIATION RULES BETWEEN SETS OF ITEMS IN LARGE DATABASES", SIGMOD RECORD, ACM, NEW YORK, NY, US LNKD- DOI:10.1145/170036.170072, (19930601), vol. 22, no. 2, ISSN 0163-5808, pages 207 - 216, XP000575841 [A] 1-57 * the whole document *
INTERNATIONAL-SEARCH-REPORT- MALOVINI ALBERTO ET AL, "Phenotype forecasting with SNPs data through gene-based Bayesian networks", BMC BIOINFORMATICS, BIOMED CENTRAL, LONDON, GB LNKD- DOI:10.1186/1471-2105-10-S2-S7, (20090205), vol. 10, no. Suppl 2, ISSN 1471-2105, page S7, XP021047454 [I] 1-57 * the whole document *
OPPOSITION- BAEZ-MONROY V.O. et al., "Principles of Employing a Self- Organizing Map as a Frequent Itemset Miner", 15th International Conference on Artificial Neural Networks: Biological Inspirations, (20050900), pages 363 - 370, XP019018161-
OPPOSITION- DIMOV R., "WEKA: Practical Machine Learning Tools and Techniques in Java", Seminar A.I. Tools , WS 2006/2007-
OPPOSITION- DIMOV R., "WEKA: Practical Machine Learning Tools and Techniques in Java", Seminar A.I. Tools , WS 2006/2007, XP055738406-
OPPOSITION- GUYON I. et al., "An introduction to variable and feature selection", Journal of Machine Learning Research, (20030300), vol. 3, pages 1157 - 1182-
OPPOSITION- GUYON I. et al., "An introduction to variable and feature selection", Journal of Machine Learning Research, (20030300), vol. 3, pages 1157 - 1182, XP058112314-
OPPOSITION- MALOVINI A. et al., "Phenotype forecasting with SNPs data through gene -based Bayesian networks", BMC Bioinformatics, (20090200), vol. 10, no. 2, pages 1 - 9-
OPPOSITION- P Sermswatrsi, Srisa-An, "A Neural-Networks Associative Classification for Association Rule Mining", WIT Transactions on Information and Communication Technologies, (20060101), vol. 37, ISSN 1743-3517, pages 93 - 102, XP055738417-
OPPOSITION- Senthil K Palanisamy, "Association Rule Based Classification", Thesis Worcester Polytechnic Institute, (20060501), pages 1 - 52-
OPPOSITION- Senthil K Palanisamy, "Association Rule Based Classification", Thesis Worcester Polytechnic Institute, (20060501), pages 1 - 52, XP055738409-
OPPOSITION- YU J. et al., "A unified mixed-model method for association mapping that account for multiple levels of relatedness", Nature Genetics, (20060200), vol. 38, no. 2, doi:10.1038/NG1702, pages 203 - 208-
OPPOSITION- ZHONG S. et al., "Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: A barley case study", Genetics, (20090400), vol. 182, no. 1, doi:10.1534/genetics.108.098277, pages 355 - 364-
OPPOSITION- YU J. et al., "A unified mixed-model method for association mapping that account for multiple levels of relatedness", Nature Genetics, (20060200), vol. 38, no. 2, doi:10.1038/NG1702, pages 203 - 208, XP002588875
OPPOSITION- TAYLOR J. et al., "Application of metabolomics to plant genotype discrimination using statistics and machine learning", Bioinformatics, (20021001), vol. 18, no. Suppl. 2, pages S241 - S248, XP055856540
OPPOSITION- LONG N. et al., "Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers", J. Anim. Breed. Genetics, (20071207), vol. 124, no. 6, pages 377 - 389, XP55856541
OPPOSITION- MALOVINI A. et al., "Phenotype forecasting with SNPs data through gene -based Bayesian networks", BMC Bioinformatics, (20090200), vol. 10, no. 2, pages 1 - 9, XP021047454
OPPOSITION- ZHONG S. et al., "Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: A barley case study", Genetics, (20090400), vol. 182, no. 1, doi:10.1534/genetics.108.098277, pages 355 - 364, XP055131917

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