메뉴 건너뛰기




Volumn 11, Issue 4, 2009, Pages 433-447

Efficient mining of multilevel gene association rules from microarray and gene ontology

Author keywords

Association rules mining; Data mining; Gene expression analysis; Gene ontology; Microarray; Multi level association rules

Indexed keywords

ANALYSIS METHOD; ASSOCIATION RULES MINING; BIOLOGICAL PROCESS; CONCEPT HIERARCHIES; DATA MINING METHODS; EMPIRICAL EVALUATIONS; EXCELLENT PERFORMANCE; GENE EXPRESSION ANALYSIS; GENE MICROARRAYS; GENE ONTOLOGY; IN-PROCESS; MINING METHODS; MULTI-LEVEL ASSOCIATION RULES; USER-SPECIFIED CONSTRAINTS;

EID: 68849087940     PISSN: 13873326     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10796-009-9156-1     Document Type: Article
Times cited : (10)

References (33)
  • 8
    • 33646708288 scopus 로고    scopus 로고
    • Liver hepcidin and stainable iron expression in biliary atresia
    • J. H. Chuang Y. H. Huang H. H. Yu V. S. Tseng 2006 Liver hepcidin and stainable iron expression in biliary atresia Pediatric Research 59 5 662 666
    • (2006) Pediatric Research , vol.59 , Issue.5 , pp. 662-666
    • Chuang, J.H.1    Huang, Y.H.2    Yu, H.H.3    Tseng, V.S.4
  • 9
    • 0037245822 scopus 로고    scopus 로고
    • Mining gene expression databases for association rules
    • C. Creighton S. Hanash 2003 mining gene expression databases for association rules Bioinformatics 19 79 86
    • (2003) Bioinformatics , vol.19 , pp. 79-86
    • Creighton, C.1    Hanash, S.2
  • 10
    • 0033569406 scopus 로고    scopus 로고
    • Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
    • T. R. Golub D. K. Slonim P. Tamayo C. Huard M. Gaasenbeek J. P. Mesirov 1999 Molecular classification of cancer: class discovery and class prediction by gene expression monitoring Science 286 531 537
    • (1999) Science , vol.286 , pp. 531-537
    • Golub, T.R.1    Slonim, D.K.2    Tamayo, P.3    Huard, C.4    Gaasenbeek, M.5    Mesirov, J.P.6
  • 11
    • 33144473447 scopus 로고    scopus 로고
    • Interactive gene clustering-a case study of breast cancer microarray data
    • A. Gruźdź A. Ihnatowicz Śl D. zak 2006 Interactive gene clustering-a case study of breast cancer microarray data Information Systems Frontiers 8 1 21 27
    • (2006) Information Systems Frontiers , vol.8 , Issue.1 , pp. 21-27
    • Gruźdź, A.1    Ihnatowiczs, A.2    Zak, S.D.3
  • 13
    • 34250013587 scopus 로고    scopus 로고
    • Large-scale regulatory network analysis from microarray data: Modified Bayesian network learning and association rule mining
    • Z. Huang J. Li H. Su G. S. Watts H. Chen 2007 Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining Decision Support Systems 43 4 1207 1225
    • (2007) Decision Support Systems , vol.43 , Issue.4 , pp. 1207-1225
    • Huang, Z.1    Li, J.2    Su, H.3    Watts, G.S.4    Chen, H.5
  • 15
    • 0038729567 scopus 로고    scopus 로고
    • Learning rule-based models of biological process from gene expression time profiles using Gene Ontology
    • T. R. Hvidsten A. Lægreid J. Komorowski 2003 Learning rule-based models of biological process from gene expression time profiles using Gene Ontology Bioinformatics 19 1116 1123
    • (2003) Bioinformatics , vol.19 , pp. 1116-1123
    • Hvidsten, T.R.1    Lægreid, A.2    Komorowski, J.3
  • 17
    • 0014129195 scopus 로고
    • Hierarchical Clustering Schemes
    • S. C. Johnson 1967 Hierarchical Clustering Schemes Psychometrika 2 241 254
    • (1967) Psychometrika , vol.2 , pp. 241-254
    • Johnson, S.C.1
  • 19
    • 33144466926 scopus 로고    scopus 로고
    • Predicting protein-protein interactions by association mining
    • M. Kotlyar I. Jurisica 2006 Predicting protein-protein interactions by association mining Information Systems Frontiers 8 1 37 47
    • (2006) Information Systems Frontiers , vol.8 , Issue.1 , pp. 37-47
    • Kotlyar, M.1    Jurisica, I.2
  • 21
    • 0036083435 scopus 로고    scopus 로고
    • Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns
    • J. Li L. Wong 2002 Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns Bioinformatics 18 725 734
    • (2002) Bioinformatics , vol.18 , pp. 725-734
    • Li, J.1    Wong, L.2
  • 22
    • 0001457509 scopus 로고
    • Some methods for classification and analysis of multivariate observations
    • Berkeley: University of California Press
    • MacQueen, J. B. (1967). Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press, 1, 281-297.
    • (1967) Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability , vol.1 , pp. 281-297
    • MacQueen J., .B.1
  • 24
    • 18144442687 scopus 로고    scopus 로고
    • Inferring subnetworks from perturbed expression profiles
    • D. Pe'er A. Regev G. Elidan N. Friedman 2001 Inferring subnetworks from perturbed expression profiles Bioinformatics 17 215 224
    • (2001) Bioinformatics , vol.17 , pp. 215-224
    • Pe'Er, D.1    Regev, A.2    Elidan, G.3    Friedman, N.4
  • 25
    • 0033027794 scopus 로고    scopus 로고
    • Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation
    • P. Tamayo 1996 Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation In: Proceedings of the National Academy of Sciences, USA 96 2907 2912
    • (1996) In: Proceedings of the National Academy of Sciences, USA , vol.96 , pp. 2907-2912
    • Tamayo, P.1
  • 26
    • 0034069495 scopus 로고    scopus 로고
    • Gene Ontology: Tool for the unification of biology
    • The Gene Ontology (GO) Consortium
    • The Gene Ontology (GO) Consortium 2000 Gene Ontology: tool for the unification of biology Nature Genetics 25 25 29
    • (2000) Nature Genetics , vol.25 , pp. 25-29
  • 27
    • 0034887203 scopus 로고    scopus 로고
    • Creating the Gene Ontology resource: Design and implementation
    • The Gene Ontology (GO) Consortium
    • The Gene Ontology (GO) Consortium 2001 Creating the Gene Ontology resource: design and implementation Genome Research 11 1425 1433
    • (2001) Genome Research , vol.11 , pp. 1425-1433
  • 30
    • 37249027262 scopus 로고    scopus 로고
    • A novel similarity-based fuzzy clustering algorithm by integrating PCM and Mountain Method
    • V. S. Tseng C.-P. Kao 2007 A novel similarity-based fuzzy clustering algorithm by integrating PCM and Mountain Method In: IEEE Transactions on Fuzzy Systems 15 6 1188 1196
    • (2007) In: IEEE Transactions on Fuzzy Systems , vol.15 , Issue.6 , pp. 1188-1196
    • Tseng, V.S.1    Kao, C.-P.2
  • 32
    • 0038491550 scopus 로고    scopus 로고
    • Ergosterol is required for targeting of tryptophan permease to the yeast plasma membrane
    • K. Umebayashi A. Nakano 2003 Ergosterol is required for targeting of tryptophan permease to the yeast plasma membrane Journal of Cell Biology 11 1117 1131
    • (2003) Journal of Cell Biology , vol.11 , pp. 1117-1131
    • Umebayashi, K.1    Nakano, A.2
  • 33
    • 38849091390 scopus 로고    scopus 로고
    • Hybrid huberized support vector machines for microarray classification and gene selection
    • L. Wang J. Zhu H. Zou 2008 Hybrid huberized support vector machines for microarray classification and gene selection Bioinformatics 24 412 419
    • (2008) Bioinformatics , vol.24 , pp. 412-419
    • Wang, L.1    Zhu, J.2    Zou, H.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.