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2006 Journal Article Mixture models for detecting differentially expressed genes in microarraysJones, L. B. T., Bean, R., McLachlan, G. J. and Zhu, J. X. (2006). Mixture models for detecting differentially expressed genes in microarrays. International Journal of Neural Systems, 16 (5), 353-362. doi: 10.1142/S0129065706000755 |
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2006 Conference Publication A mixture model with random-effects components for clustering correlated gene-expression profilesNg, S. K., McLachlan, G. J., Wang, K., Jones, L. Ben-Tovim and Ng, S. W. (2006). A mixture model with random-effects components for clustering correlated gene-expression profiles. doi: 10.1093/bioinformatics/btl165 |
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2006 Journal Article A Score Test for Zero-Inflation in Correlated Count DataXiang, Liming, Lee, Andy H., Yau, Kelvin K. W. and McLachlan, Geoffrey J. (2006). A Score Test for Zero-Inflation in Correlated Count Data. Statistics In Medicine, 25 (10), 1660-1671. doi: 10.1002/sim.2308 |
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2006 Journal Article An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource UtilizationNg, S. K., McLachlan, G. J. and Lee, A. H. (2006). An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization. Artificial Intelligence In Medicine, 36 (3), 257-267. doi: 10.1016/j.artmed.2005.07.003 |
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2006 Journal Article Robust cluster analysis via mixture modelsMcLachlan, G J, Ng, S K and Bean, R W (2006). Robust cluster analysis via mixture models. Austrian Journal of Statistics, 35 (2 & 3), 157-174. |
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2006 Journal Article Selection bias in working wit the top genes in supervised classification of tissue samplesZhu, X., Ambroise, C and McLachlan, G J (2006). Selection bias in working wit the top genes in supervised classification of tissue samples. Statistical Methodology, 3 (1), 29-41. doi: 10.1016/j.stamet.2005.09.011 |
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2006 Conference Publication Issues of robustness and high dimensionality in cluster analysisBasford, Kaye, McLachlan, Geoff and Bean, Richard (2006). Issues of robustness and high dimensionality in cluster analysis. 17th Symposium on Computational Statistics (COMSTAT 2006), Rome, Italy, 28 August - 1 September 2006. Rome, Italy: Physica-Verlag. doi: 10.1007/978-3-7908-1709-6_1 |
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2006 Journal Article A simple implementation of a normal mixture approach to differential gene expression in multiclass microarraysMcLachlan, GJ, Bean, RW and Jones, LBT (2006). A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinformatics, 22 (13), 1608-1615. doi: 10.1093/bioinformatics/btl148 |
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2006 Conference Publication Multilevel modelling for inference of genetic regulatory networksNg, Shu-Kay, Wang, Kui and McLachlan, Geoffrey J. (2006). Multilevel modelling for inference of genetic regulatory networks. Complex Systems, Brisbane, Australia, 11-14 December 2005. Bellingham, WA, United States: SPIE - International Society for Optical Engineering. doi: 10.1117/12.638449 |
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2006 Journal Article Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zerosLee, AH, Wang, K, Scott, JA, Yau, KKW and McLachlan, GJ (2006). Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros. Statistical Methods In Medical Research, 15 (1), 47-61. doi: 10.1191/0962280206sm429oa |
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2006 Journal Article A Mixture model with random-effects components for clustering correlated gene-expression profilesNg, SK, McLachlan, GJ, Wang, K, Jones, LBT and Ng, SW (2006). A Mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinformatics, 22 (14), 1745-1752. doi: 10.1093/bioinformatics/btl165 |
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2006 Conference Publication Clustering replicated microarray data in mixtures of random effects models for varius covariance structuresNg, S K, McLachlan, G J, Bean, R W and NG, SW (2006). Clustering replicated microarray data in mixtures of random effects models for varius covariance structures. 2006 Workshop on Intelligent Systems for Bioinformatics (WISB, Hobart, Australia, 4 December 2006. Sydney: The Australian Computer Society. |
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2005 Journal Article Using mixture models to detect differentially expressed genesMcLachlan, G. J., Bean, R. W., Jones, L. and Zhu, J. X. (2005). Using mixture models to detect differentially expressed genes. Australian Journal Of Experimental Agriculture, 45 (7-8), 859-866. doi: 10.1071/EA05051 |
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2005 Conference Publication Normalized Gaussian Networks with Mixed Feature DataNg, A. S. K. and McLachlan, G. J. (2005). Normalized Gaussian Networks with Mixed Feature Data. 18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, 5-9 Dec 2005. Berlin, Germany: Springer-Verlag. doi: 10.1007/11589990_101 |
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2005 Journal Article Use of the EM algorithm to detect QTL affecting multiple-traits in an across half-sib family analysisKerr, R. J., McLachlan, G. J. and Henshall, J. M. (2005). Use of the EM algorithm to detect QTL affecting multiple-traits in an across half-sib family analysis. Genetics Selection Evolution, 37 (1), 83-103. doi: 10.1051/gse:2004037 |
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2005 Conference Publication Application of mixture models to detect differentially expressed genesJones, LBT, Bean, R, McLachlan, G and Zhu, J (2005). Application of mixture models to detect differentially expressed genes. Berlin: Springer-Verlag Berlin. doi: 10.1007/11508069_55 |
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2005 Journal Article Cluster analysis of high-dimensional data: A case studyBean, R and McLachlan, G (2005). Cluster analysis of high-dimensional data: A case study. Intelligent Data Engineering And Automated Learning Ideal 2005, Proceedings, 3578 (-), 302-310. |
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2005 Book Chapter Use of microarray data via model-based classification in the study and prediction of survival from lung cancerJones, L., Ng, S., Ambroise, C, Monico, K. A., Khan, N. and McLachlan, G. J. (2005). Use of microarray data via model-based classification in the study and prediction of survival from lung cancer. Methods of microarray data analysis IV. (pp. 163-173) edited by Jennifer S. Shoemaker and Simon M. Lin. New York, USA: Springer. doi: 10.1007/0-387-23077-7_13 |
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2005 Conference Publication Mixture Model-based Statistical Pattern Recognition of Clustered or Longitudinal DataNg, A.S.K. and McLachlan, G. J. (2005). Mixture Model-based Statistical Pattern Recognition of Clustered or Longitudinal Data. WDIC2005, Griffith University, 21 February 2005. Brisbane, Australia: Australian Pattern Recognition Society. |
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2004 Journal Article Modelling the Distribution of Ischaemic Stroke-Specific Survival Time Using an EM-based Mixture Approach with Random Effects AdjustmentNg, S. K., McLachlan, G. J., Yau, K. K. W. and Lee, A. H. (2004). Modelling the Distribution of Ischaemic Stroke-Specific Survival Time Using an EM-based Mixture Approach with Random Effects Adjustment. Statistics In Medicine, 23 (17), 2729-2744. doi: 10.1002/sim.1840 |
