Professor Geoffrey McLachlan
Professor

Geoffrey McLachlan

Email: 
Phone: 
+61 7 336 52150

Background

Professor Geoffrey McLachlan's research interests are in: data mining, statistical analysis of microarray, gene expression data, finite mixture models and medical statistics.

Professor McLachlan received his PhD from the University of Queensland in 1974 and his DSc from there in 1994. His current research projects in statistics are in the related fields of classification, cluster and discriminant analyses, image analysis, machine learning, neural networks, and pattern recognition, and in the field of statistical inference. The focus in the latter field has been on the theory and applications of finite mixture models and on estimation via the EM algorithm.

A common theme of his research in these fields has been statistical computation, with particular attention being given to the computational aspects of the statistical methodology. This computational theme extends to Professor McLachlan's more recent interests in the field of data mining.

He is also actively involved in research in the field of medical statistics and, more recently, in the statistical analysis of microarray gene expression data.

Availability

Professor Geoffrey McLachlan is:
Available for supervision
Media expert

Fields of research

Statistics

Qualifications

  • Bachelor (Honours) of Science (Advanced), The University of Queensland
  • Doctor of Philosophy, The University of Queensland
  • Doctoral Diploma of Science (Advanced), The University of Queensland
  • Australian Mathematical Society, Australian Mathematical Society

Search Professor Geoffrey McLachlan’s works on UQ eSpace

370 works between 1972 and 2024
All (370) Journal Article (249) Book Chapter (34) Conference Publication (75) Book (9) Other Outputs (2) Edited Outputs (1)

201 - 220 of 370 works

2006

Journal Article

Mixture models for detecting differentially expressed genes in microarrays

Jones, 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

Mixture models for detecting differentially expressed genes in microarrays

2006

Conference Publication

A mixture model with random-effects components for clustering correlated gene-expression profiles

Ng, 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

A mixture model with random-effects components for clustering correlated gene-expression profiles

2006

Journal Article

A Score Test for Zero-Inflation in Correlated Count Data

Xiang, 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

A Score Test for Zero-Inflation in Correlated Count Data

2006

Journal Article

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

McLachlan, 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

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

2006

Conference Publication

Multilevel modelling for inference of genetic regulatory networks

Ng, 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

Multilevel modelling for inference of genetic regulatory networks

2006

Journal Article

Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros

Lee, 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

Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros

2006

Journal Article

A Mixture model with random-effects components for clustering correlated gene-expression profiles

Ng, 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

A Mixture model with random-effects components for clustering correlated gene-expression profiles

2006

Conference Publication

Clustering replicated microarray data in mixtures of random effects models for varius covariance structures

Ng, 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.

Clustering replicated microarray data in mixtures of random effects models for varius covariance structures

2006

Journal Article

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Ng, 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

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

2006

Journal Article

Robust cluster analysis via mixture models

McLachlan, 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.

Robust cluster analysis via mixture models

2006

Journal Article

Selection bias in working wit the top genes in supervised classification of tissue samples

Zhu, 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

Selection bias in working wit the top genes in supervised classification of tissue samples

2006

Conference Publication

Issues of robustness and high dimensionality in cluster analysis

Basford, 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

Issues of robustness and high dimensionality in cluster analysis

2005

Journal Article

Using mixture models to detect differentially expressed genes

McLachlan, 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

Using mixture models to detect differentially expressed genes

2005

Conference Publication

Normalized Gaussian Networks with Mixed Feature Data

Ng, 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

Normalized Gaussian Networks with Mixed Feature Data

2005

Journal Article

Use of the EM algorithm to detect QTL affecting multiple-traits in an across half-sib family analysis

Kerr, 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

Use of the EM algorithm to detect QTL affecting multiple-traits in an across half-sib family analysis

2005

Conference Publication

Application of mixture models to detect differentially expressed genes

Jones, 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

Application of mixture models to detect differentially expressed genes

2005

Journal Article

Cluster analysis of high-dimensional data: A case study

Bean, 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.

Cluster analysis of high-dimensional data: A case study

2005

Book Chapter

Use of microarray data via model-based classification in the study and prediction of survival from lung cancer

Jones, 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

Use of microarray data via model-based classification in the study and prediction of survival from lung cancer

2005

Conference Publication

Mixture Model-based Statistical Pattern Recognition of Clustered or Longitudinal Data

Ng, 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.

Mixture Model-based Statistical Pattern Recognition of Clustered or Longitudinal Data

2004

Journal Article

Modelling the Distribution of Ischaemic Stroke-Specific Survival Time Using an EM-based Mixture Approach with Random Effects Adjustment

Ng, 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

Modelling the Distribution of Ischaemic Stroke-Specific Survival Time Using an EM-based Mixture Approach with Random Effects Adjustment

Current funding

  • 2023 - 2026
    A Novel Approach to Semi-Supervised Statistical Machine Learning
    ARC Discovery Projects
    Open grant

Past funding

  • 2018 - 2022
    Classification methods for providing personalised and class decisions
    ARC Discovery Projects
    Open grant
  • 2017 - 2024
    ARC Training Centre for Innovation in Biomedical Imaging Technology
    ARC Industrial Transformation Training Centres
    Open grant
  • 2017 - 2020
    Power Quality Monitoring of Grids with High Penetration of Power Converters
    ARC Linkage Projects
    Open grant
  • 2017 - 2020
    Expanding the Role of Mixture Models in Statistical Analyses of Big Data
    ARC Discovery Projects
    Open grant
  • 2015 - 2018
    Gene expression profiling in critically ill patients with septic shock: The ADRENAL-GEPS Study
    NHMRC Project Grant
    Open grant
  • 2015 - 2017
    Large-Scale Statistical Inference: Multiple Testing
    ARC Discovery Projects
    Open grant
  • 2014 - 2016
    System to Synapse
    ARC Linkage Projects
    Open grant
  • 2014 - 2017
    Advanced Mixture Models for the Analysis of Modern-Day Data
    ARC Discovery Projects
    Open grant
  • 2012 - 2014
    System to synapse: a small animal imaging suite
    UQ Collaboration and Industry Engagement Fund
    Open grant
  • 2012 - 2014
    Joint Clustering and Matching of Multivariate Samples Across Objects
    ARC Discovery Projects
    Open grant
  • 2012 - 2014
    Statistical Modelling of Complex, High-Dimensional Data
    Vice-Chancellor's Senior Research Fellowship
    Open grant
  • 2011 - 2013
    A New Approach to Fast Matrix Factorization for the Statistical Analysis of High-Dimensional Data
    ARC Discovery Projects
    Open grant
  • 2008 - 2010
    Mixture models for high-dimensional clustering with applications to tumour classification, network intrusion, and text classification
    ARC Discovery Projects
    Open grant
  • 2007 - 2011
    Multivariate Methods for the Analysis of Microarray Gene-Expression Data with Applications to Cancer Diagnostics
    ARC Discovery Projects
    Open grant
  • 2007 - 2009
    Noncoding RNAs as prognostic markers and therapeutic targets in breast cancer
    NHMRC Project Grant
    Open grant
  • 2004
    ARC Network in Imaging Science and Technology
    ARC Seed Funding for Research Networks
    Open grant
  • 2004
    ARC Research Network in Microarray Technology
    ARC Seed Funding for Research Networks
    Open grant
  • 2003 - 2010
    ARC Centre of Excellence in Bioinformatics
    ARC Centres of Excellence
    Open grant
  • 2003
    Classification of Microarray Gene-Expression Data
    ARC Discovery Projects
    Open grant
  • 2003
    Classification of Microarray Gene-expression Data
    UQ External Support Enabling Grant
    Open grant
  • 2003
    Unsupervised learning of finite mixture models in data mining applications
    ARC Discovery Projects
    Open grant
  • 2000 - 2002
    Classification of Multiply Observed Features in Terms of Fitted Densities
    ARC Australian Research Council (Large grants)
    Open grant
  • 2000 - 2002
    On Algorithms for the Automatic Analysis and Segmentation of Correlated Images
    ARC Australian Research Council (Large grants)
    Open grant
  • 1999 - 2001
    Artificial Neural Networks and the EM Algorithm
    ARC Australian Research Council (Large grants)
    Open grant
  • 1999
    On mixture regression models with constrained components for application to failure data on heart valves
    ARC Australian Research Council (Small grants)
    Open grant
  • 1998
    Robust cluster analysis
    ARC Australian Research Council (Small grants)
    Open grant
  • 1997
    On mixture models in medical imaging
    ARC Australian Research Council (Small grants)
    Open grant
  • 1997 - 1999
    The Analysis of Plant Adaptation Data with Emphasis on Unbalanced Sets
    ARC Australian Research Council (Large grants)
    Open grant
  • 1995 - 1997
    Approximation of multi-dimensional functions for curve fitting and model building
    ARC Australian Research Council (Large grants)
    Open grant

Availability

Professor Geoffrey McLachlan is:
Available for supervision

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Supervision history

Current supervision

Completed supervision

Enquiries

Contact Professor Geoffrey McLachlan directly for media enquiries about:

  • Bioinformatics
  • Computation - statistics
  • Computer learning
  • Data mining
  • Gene expression data
  • Image analysis - statistics
  • Machine learning
  • Neural networks
  • Pattern recognition - statistics
  • Statistical methodology
  • Statistics

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