Mining Gene Expression Data using Domain Knowledge
暂无分享,去创建一个
Laurent Brisson | Martine Collard | Nicolas Pasquier | Claude Pasquier | Nicolas Pasquier | M. Collard | C. Pasquier | Laurent Brisson
[1] John L. Pfaltz,et al. Closed Set Mining of Biological Data , 2002, BIOKDD.
[2] Hsinchun Chen,et al. Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining , 2007, Decis. Support Syst..
[3] Raymond J. Mooney,et al. Integrating constraints and metric learning in semi-supervised clustering , 2004, ICML.
[4] Haidong Wang,et al. Discovering molecular pathways from protein interaction and gene expression data , 2003, ISMB.
[5] Roded Sharan,et al. Discovering statistically significant biclusters in gene expression data , 2002, ISMB.
[6] Rainer Breitling,et al. Iterative Group Analysis (iGA): A simple tool to enhance sensitivity and facilitate interpretation of microarray experiments , 2004, BMC Bioinformatics.
[7] M. Thattai,et al. Intrinsic noise in gene regulatory networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[8] Ricardo Martínez,et al. Extracted Knowledge Interpretation in mining biological data: a survey , 2007, RCIS.
[9] L. Ohno-Machado,et al. Comparison of hybridization-based and sequencing-based gene expression technologies on biological replicates , 2007, BMC Genomics.
[10] John F. Roddick,et al. Association mining , 2006, CSUR.
[11] Gerhard Tutz,et al. A CART-based approach to discover emerging patterns in microarray data , 2003, Bioinform..
[12] Thomas Lengauer,et al. A new measure for functional similarity of gene products based on Gene Ontology , 2006, BMC Bioinformatics.
[13] Laurent Brisson,et al. An Ontology Driven Data Mining Process , 2008, ICEIS.
[14] Huan Liu,et al. Subspace clustering for high dimensional data: a review , 2004, SKDD.
[15] Le Gruenwald,et al. Microarray gene expression data association rules mining based on JG-Tree , 2003, 14th International Workshop on Database and Expert Systems Applications, 2003. Proceedings..
[16] Anna Maddalena. Pattern Based Management: Data Models and Architectural Aspects , 2004, EDBT Workshops.
[17] Arlindo L. Oliveira,et al. Biclustering algorithms for biological data analysis: a survey , 2004, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[18] Lei Liu,et al. Subspace clustering for microarray data analysis:multiple criteria and significance assessment , 2004, Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004..
[19] Patrick Meyer,et al. On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid , 2008, Eur. J. Oper. Res..
[20] Lothar Thiele,et al. A systematic comparison and evaluation of biclustering methods for gene expression data , 2006, Bioinform..
[21] Dekang Lin,et al. An Information-Theoretic Definition of Similarity , 1998, ICML.
[22] M. Rattray,et al. A comparison of microarray and MPSS technology platforms for expression analysis of Arabidopsis , 2007, BMC Genomics.
[23] José María Carazo,et al. BMC Bioinformatics BioMed Central Methodology article Integrated analysis of gene expression by association rules discovery , 2022 .
[24] Chad Creighton,et al. Mining gene expression databases for association rules , 2003, Bioinform..
[25] C. Pasquier. Biological data integration using Semantic Web technologies. , 2008, Biochimie.
[26] Rithy K. Roth,et al. Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays , 2000, Nature Biotechnology.
[27] Ronald W. Davis,et al. Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.
[28] D. Pe’er,et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data , 2003, Nature Genetics.
[29] David W. Conrath,et al. Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy , 1997, ROCLING/IJCLCLP.
[30] T. Mcintosh,et al. High Confidence Rule Mining for Microarray Analysis , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[31] Dennis McLeod,et al. Subspace Clustering of Microarray Data Based on Domain Transformation , 2006, VDMB.
[32] Ricardo Martínez,et al. GenMiner: Mining Informative Association Rules from Genomic Data , 2007, 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007).
[33] Klaus R. Dittrich,et al. Three decades of data integration - All problems solved? , 2004, IFIP Congress Topical Sessions.
[34] A. Kerlavage,et al. Complementary DNA sequencing: expressed sequence tags and human genome project , 1991, Science.
[35] M. Daly,et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes , 2003, Nature Genetics.
[36] Ji Huang,et al. [Serial analysis of gene expression]. , 2002, Yi chuan = Hereditas.
[37] T. Werner. Bioinformatics applications for pathway analysis of microarray data. , 2008, Current opinion in biotechnology.
[38] Daniel Hanisch,et al. Co-clustering of biological networks and gene expression data , 2002, ISMB.
[39] Carolina Ruiz,et al. Distance-enhanced association rules for gene expression , 2003, BIOKDD.
[40] Simon J Davis,et al. Deep analysis of cellular transcriptomes – LongSAGE versus classic MPSS , 2007, BMC Genomics.
[41] Musa H. Asyali,et al. Gene Expression Profile Classification: A Review , 2006 .
[42] Sang-Ho Lee,et al. Application of Emerging Patterns for Multi-source Bio-Data Classification and Analysis , 2005, ICNC.
[43] D. Botstein,et al. Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.
[44] Jian Pei,et al. Mining cross-graph quasi-cliques in gene expression and protein interaction data , 2005, 21st International Conference on Data Engineering (ICDE'05).
[45] Attila Gyenesei,et al. Mining co-regulated gene profiles for the detection of functional associations in gene expression data , 2007, Bioinform..
[46] Elisa Bertino,et al. Towards a Logical Model for Patterns , 2003, ER.
[47] Anthony K. H. Tung,et al. Carpenter: finding closed patterns in long biological datasets , 2003, KDD '03.
[48] Lei Liu,et al. Subspace clustering for microarray data analysis:multiple criteria and significance assessment , 2004 .
[49] Anthony K. H. Tung,et al. Mining top-K covering rule groups for gene expression data , 2005, SIGMOD '05.
[50] Philip Resnik,et al. Using Information Content to Evaluate Semantic Similarity in a Taxonomy , 1995, IJCAI.
[51] Christiane Fellbaum,et al. Combining Local Context and Wordnet Similarity for Word Sense Identification , 1998 .
[52] Huiqing Liu,et al. Discovery of significant rules for classifying cancer diagnosis data , 2003, ECCB.
[53] Wynne Hsu,et al. Finding Interesting Patterns Using User Expectations , 1999, IEEE Trans. Knowl. Data Eng..
[54] Carole A. Goble,et al. State of the nation in data integration for bioinformatics , 2008, J. Biomed. Informatics.
[55] M. Ashburner,et al. Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.
[56] Yong Yu,et al. Conceptual Graph Matching for Semantic Search , 2002, ICCS.
[57] C. Becquet,et al. Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data , 2002, Genome Biology.
[58] Björn Olsson,et al. Using functional annotation to improve clusterings of gene expression patterns , 2002, Inf. Sci..
[59] Carole A. Goble,et al. Semantic Similarity Measures as Tools for Exploring the Gene Ontology , 2002, Pacific Symposium on Biocomputing.
[60] Jason E. Stewart,et al. Minimum information about a microarray experiment (MIAME)—toward standards for microarray data , 2001, Nature Genetics.
[61] Kei-Hoi Cheung,et al. Advancing translational research with the Semantic Web , 2007, BMC Bioinformatics.
[62] Engelbert Mephu Nguifo,et al. Frequent closed itemset based algorithms: a thorough structural and analytical survey , 2006, SKDD.
[63] Ricardo Martínez,et al. Co-expressed gene groups analysis (CGGA): An automatic tool for the interpretation of microarray experiments , 2006 .
[64] Zhaohong Deng,et al. Clustering Analysis of Gene Expression Data based on Semi-supervised Visual Clustering Algorithm , 2006, Soft Comput..
[65] Jinyan Li,et al. Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns , 2002, Bioinform..
[66] Abraham Silberschatz,et al. What Makes Patterns Interesting in Knowledge Discovery Systems , 1996, IEEE Trans. Knowl. Data Eng..
[67] G. A. Whitmore,et al. Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[68] Jian Pei,et al. Mining gene–sample–time microarray data: a coherent gene cluster discovery approach , 2007, Knowledge and Information Systems.
[69] Gediminas Adomavicius,et al. Handling very large numbers of association rules in the analysis of microarray data , 2002, KDD.
[70] Seon-Young Kim,et al. PAGE: Parametric Analysis of Gene Set Enrichment , 2005, BMC Bioinform..