Griffith University

Book chapters  

• McLachlan GJ, Ng SK (2009) EM. In The Top-Ten Algorithms in Data Mining, X Wu and V Kumar (Eds.). Boca Raton, Florida: Chapman & Hall/CRC, 93-115.
• McLachlan GJ, Bean RW, Ng SK (2008) Clustering of microarray data via mixture models. In Statistical Advances in Biomedical Sciences: Clinical Trials, Epidemiology, Survival Analysis, and Bioinformatics, A Biswas, S Datta, JP Fine, MR Segal (Eds.). Hoboken, New Jersey: Wiley, 365-384.
• Ehrlich JR, Tang L, Caiazzo Jr RJ, Cramer DW, Ng SK, Ng SW, Liu BCS (2008) The reverse capture autoantibody microarray: an innovative approach to profiling the autoantibody response to tissue-derived native antigens. In Tissue Proteomics: Pahtways, Biomarkers, and Drug Discovery, BCS Liu and JR Ehrlich (Eds.). Totowa, New Jersey: Humana Press, 175-192.
  
• McLachlan GJ, Bean RW, Ng SK (2008) Clustering. In Bioinformatics, Vol. 2: Structure, Function, and Applications, JM Keith (Ed.). Totowa, New Jersey: Humana Press, 423-439.
  
• Ben-Tovim Jones L, Ng SK, Ambroise C, Monico K, Khan N, McLachlan GJ (2005) Use of microarray data via model-based classification in the study and prediction of survival from lung cancer. In Methods of Microarray Data Analysis IV, JS Shoemaker, SM Lin (Eds.). New York: Springer, 163-173.
  
• Ng SK, Krishnan T, McLachlan GJ (2004) The EM algorithm. In Handbook of Computational Statistics Vol. 1, J Gentle, W Hardle, Y Mori (Eds). New York: Springer-Verlag, 137-168.
  
• McLachlan GJ, Ng SK, Peel D (2003) On clustering by mixture models. In Studies in Classification, Data Analysis, and Knowledge Organization: Exploratory Data Analysis in Empirical Research, M Schwaiger and O Opitz (Eds.). Berlin: Springer-Verlag, 141-148.

Refereed journal articles 

• Holden L, Scuffham PA, Hilton MF, Muspratt A, Ng S-K, Whiteford HA (2011) Patterns of multimorbidity in working Australians. Population Health Metrics. 9(1). 1-5.
• Turkstra E, Ng S-K, Scuffham PA (2011) A mixed treatment comparison of the short-term efficacy of biologic disease modifying anti-rheumatic drugs in established rheumatoid arthritis. Current Medical Research & Opinion. 27(10). 1885-1897.
• Moore R, Yelland M, Ng S-K (2011) Moving with the times - Familiarity versus formality in Australian general practice. Australian Family Physician. 40(12). 1004-1007.
• Nikulin V, Huang TH, Ng SK, Rathnayake SI, McLachlan GJ (2011) A very fast algorithm for matrix factorization. Statistics and Probability Letters. 81(7). 773-782.
• Ng SK, Olog A, Spinks AB, Cameron CM, Searle J, McClure RJ (2010) Risk factors and obstetric complications of large for gestational age births with adjustments for community effects: results from a new cohort study. BMC Public Health. 10(460). 1-10.
  
• Ng SK (2010) To Predict Disease Outcome: Clinical Risk Factors Plus Genetic-staging for Cancer. Pakistan Journal of Statistics. 26(1). 171-185.
• Tang L, Yang J, Ng SK, Rodriguez N, Choi P-W, Vitonis A, Wang K, McLachlan GJ, Caiazzo RJ Jr, Liu, BCS, Welch WR, Cramer DW, Berkowitz RS, Ng S-W (2010) Autoantibody profiling to identify biomarkers of key pathogenic pathways in mucinous ovarian cancer. European Journal of Cancer, 46, 170-179.
• Nikulin V, McLachlan GJ, Ng SK (2009) Ensemble approach for the classification of imbalanced data.  Lecture Notes in Artificial Intelligence, 5866, 291-300.
• Wright S, Yelland M, Heathcote K, Ng S (2009) Fear of needles in general practice; nature and prevalence. Australian Family Physician, 38(3), 172-176.
  
• Lee Andy H,Yun Zhao,Yau Kelvin KW, Ng SK (2009) A computer graphical user interface for survival mixture modelling of recurrent infections. Computers in Biology and Medicine, 39, 301-307.
  
• Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng SK, Liu B, Yu PS, Zhou Z-H, Steinbach M, Hand DJ, Steinberg D (2008) Top 10 algorithms in data mining. Knowledge and Information Systems, 14, 1-37.
  
• McLachlan GJ, Wang K, Ng SK (2008) Large-scale simultaneous inference with applications to the detection of differential expression with microarray data (with discussion). Statistica, 68, 1-30.
• Ng SK, McLachlan GJ (2007) Extension of mixture-of-experts networks for binary classification of hierarchical data. Artificial Intelligence in Medicine, 41, 57-67.
  
• Lee AH, Wang K, Yau KKW, McLachlan GJ, Ng SK (2007) Maternity length of stay modeling by gamma mixture regression with random effects.  Biometrical Journal, 49, 750-764.
• Ng SK, McLachlan GJ, Wang K, Ben-Tovim L, Ng SW (2006) A mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinformatics, 22, 1745-1752.
• Ng SK, McLachlan GJ, Lee AH (2006) An incremental EM-based learning approach for on-line prediction of hospital resource utilization. Artificial Intelligence in Medicine, 36, 257-267.
• Huang KC, Park DC, Ng SK, Lee JY, Ni X, Ng WC, Bandera CA, Welch WR, Berkowitz RS, Mok SC, Ng SW (2006) Selenium binding protein 1 in ovarian cancer.  International Journal of Cancer, 118, 2433-2440.
• McLachlan GJ, Ng SK, Bean RW (2006) Robust cluster analysis via mixture models.  Austrian Journal of Statistics, 35, 157-174.
• Ng SK, McLachlan GJ (2005) Normalized Gaussian networks with mixed feature data. Lecture Notes in Artificial Intelligence, 3809, 879-882.
• Ng SK, McLachlan GJ, Yau KKW, Lee AH (2004) Modelling the distribution of ischaemic stroke-specific survival time using an EM-based mixture approach with random effects adjustment. Statistics in Medicine, 23, 2729-2744.
• Ni X, Zhang W, Huang KC, Wany Y, Ng SK, Mok SC, Berkowitz RS, Ng SW (2004) Characterization of human kallikrein 6/protease M expression in ovarian cancer.  British Journal of Cancer, 91, 725-731.
• Ng SK, McLachlan GJ (2004) Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images. Pattern Recognition, 37, 1573-1589.
  
• Ng SK, McLachlan GJ (2004) Using the EM algorithm to train neural networks: misconceptions and a new algorithm for multiclass classification. IEEE Transactions on Neural Networks, 15, 738-749.
  
• Ng SK, Yau KKW, Lee AH (2003) Modelling inpatient length of stay by a hierarchical mixture regression via the EM algorithm. Mathematical and Computer Modelling, 37, 365-375.
  
• Ng SK, McLachlan GJ (2003) On some variants of the EM algorithm for the fitting of finite mixture models. Austrian Journal of Statistics, 32, 143-161.
  
• Ng SK, McLachlan GJ (2003) An EM-based semiparametric mixture model approach to the regression analysis of competing-risks data.  Statistics in Medicine, 22, 1097-1111.
• Yau KKW, Lee A, Ng SK (2003) Finite mixture regression model with random effects: Application to neonatal hospital length of stay. Computational Statistics and Data Analysis, 41, 359-366.
• Ng SK, McLachlan GJ (2003) On the choice of the number of blocks with the incremental EM algorithm for the fitting of normal mixtures. Statistics and Computing, 13, 45-55.
• Huang KC, Rao PH, Lau CC, Heard E, Ng SK, Brown C, Mok SC, Berkowitz RS, Ng SW (2002) Relationship of XIST expression and responses of ovarian cancer to chemotherapy.  Molecular Cancer Therapeutics, 1, 769-776.
• Yau KKW, Lee AH, Ng SK (2002) A zero-augmented gamma mixed model to analyse longitudinal data with many zeros.  Australian and New Zealand Journal of Statistics, 44, 177-183.
• Ng SK, OBrien MF, Harrocks S and McLachlan GJ (2002) The influence of patient age and implantation technique on the probability of re-replacement of the allograft aortic valve (with discussion). Journal of Heart Valve Disease, 11, 217-223.
• Lee AH, Xiao J, Codde JP, Ng SK (2002) Public versus private hospital maternity length of stay: a gamma mixture modelling approach.  Health Services Management Research, 15, 46-54.
• Lee AH, Ng SK, Yau KKW (2001) Determinants of maternity length of stay: a gamma mixture risk-adjusted model.  Health Care Management Science, 4, 249-255.
• Yau KKW, Ng SK (2001) Long-term survivor mixture model with random effects: Application to a multi-centre clinical trial of carcinoma.  Statistics in Medicine, 20, 1591-1607.
• Ng SK, McLachlan GJ, McGiffin DC, OBrien MF (1999) Constrained mixture models in competing risks problems.  Environmetrics, 10, 753-767.
• Yau KKW, Ng SK, Cheung MT, Tung MC (1999) Estimation of surgeon effects in the analysis of post-operative colorectal cancer patients data.  Journal of Applied Statistics, 26, 257-272.
• Ng SK, McLachlan GJ (1998) On modifications to the long-term survival mixture model in the presence of competing risks.  Journal of Statistical Computation and Simulation, 61, 77-96.
• McLachlan GJ, Ng SK, Adams P, McGiffin DC, Gailbraith AJ (1997) An algorithm for fitting mixtures of Gompertz distributions to censored survival data. Journal of Statistical Software, 2, issue 7.

Refereed conference papers

• Wang K, Ng SK, McLachlan GJ (2009) Multivariate skew t mixture models: applications to fluorescence-activated cell sorting data. In Proceedings of DICTA, Conference of Digital Image Computing: Techniques and Applications, Melbourne. Los Alamitos, California: IEEE Computer Society, 526-531.
• Lee AH, Zhao Y, Yau KKW, Ng SK (2009) Survival mixture modelling of recurrent infections. In Proceedings of IASC: Joint Meeting of 4th World Conference of the IASC and 6th Conference of the Asian Regional Section of the IASC on Computational Statistics & Data Analysis, December 5-8, 2009, Yokohama, Japan, 1008-1014.
• McLachlan GJ, Ng SK, Wang K (2008) Clustering via mixture regression models with random effects. In Proceedings of COMPSTAT 2008, 18th Symposium of Computational Statistics, P Brito (Ed). Heidelberg: Springer, 397-407
• Ng SK, McLachlan GJ, Bean RW, Ng SW (2006) Clustering replicated microarray data via mixtures of random effects models for various covariance structures.  In Conferences in Research and Practice in Information Technology, M Boden and TL Bailey (Eds). Sydney: The Australian Computer Society, 29-33
• Ng SK, Wang K, McLachlan GJ (2005) Multilevel modeling for the inference of genetic regulatory networks.  In Proceedings of SPIE, SPIE International Symposium on Microelectronics, MEMS, and Nanotechnology, Vol 6039. A Bender (Ed). Bellingham, Washington: International Society for Optical Engineering, 60390S-1 to 60390S-12
• Ng SK, McLachlan GJ (2005) Mixture model-based statistical pattern recognition of clustered or longitudinal data.  In Proceedings of WDIC 2005, APRS Workshop on Digital Image Computing, BC Lovell and AJ Maeder (Eds). Brisbane: Australian Pattern Recognition Society, 139-144
• Ben-Tovim Jones L, Ng SK, Monico K, McLachlan GJ (2004) Linking gene-expression experiments with survival-time data. In Statistical Modelling, Proceedings of the 19th International Workshop on Statistical Modelling. A Biggeri, E Dreassi, C Lagazio and M Marchi (Eds). Florence: Firenze University Press, 71-75
• Ng SK, McLachlan GJ (2003) Robust estimation in Gaussian mixtures using multiresolution kd-trees.  In Proceedings of DICTA, 7th Conference of Digital Image Computing: Techniques and Applications Vol 1, C Sun, H Talbot, S Ourselin and T Adriaansen (Eds). Sydney: Australian Pattern Recognition Society, 145-154
• Kim SG, Ng SK, McLachlan GJ, Wang D (2003) Segmentation of brain MR images with bias-field correction.  In Proceedings of WDIC, APRS Workshop on Digital Image Computing, BC Lovell and A Maeder (Eds.). Brisbane: Australian Pattern Recognition Society, 3-8
• Ng SK, McLachlan GJ (2002) On speeding up the EM algorithm in pattern recognition: A comparison of incremental and multiresolution kd-tree-based approaches.  In Proceedings of DICTA 2002, 6th Conference of Digital Image Computing: Techniques and Applications, D Suter and A Bab-Hadiashar (Eds.). Melbourne: Australian Pattern Recognition Society, 116-121
• McLachlan GJ, Ng SK, Galloway G and Wang D (1996) Clustering of  magnetic resonance images.  Proceedings of the American Statistical Association (Statistical Computing Section), Chicago, August, Alexandria, Virginia: American Statistical Association, 12-17
• McLachlan GJ, Ng SK, Galloway G, Wang D (1996) A mixture model-based approach to the clustering of MR images of the human brain.  2nd Image Segmentation Workshop, University of Technology, Sydney, December, 33-39. Australian Pattern Recognition Society
• Ng SK, McLachlan GJ, Galloway G, Rose SE (1995) A mixture model approach to segmentation of magnetic resonance images.  Proceedings of DICTA 95, 3rd Conference of Digital Image Computing: Techniques and Applications, University of Queensland, December, 588-593.  Brisbane: Australian Pattern Recognition Society

Non-refereed conference papers

• Ni X, Huang KC, Ng WC, Mok SC, Ng SK, Hiroaki K, Berkowitz RS, Ng SW (2005) Transcriptional and proteomic profiling of secreted and membrane proteins in ovarian cancer. In Proceedings of Keystone Symposia Meeting on Proteomics and Bioinformatics, #227, 55
• Huang KC, Rao PH, Lau CC, Heard E, Ng SK, Brown C, Mok SC, Berkowitz RS, Ng SW (2002) Expression profiling for drug resistance in ovarian cancer. In  Proceedings of the Second International Conference on Ovarian Cancer, Houston, USA, 117
• Lee AH, Xiao J, Codde JP, Ng SK (1999) Gamma mixture modelling of length of stay for the public and private hospitals of Western Australia.  In Proceedings of the 11th CASEMIX conference in Australia, Darwin August. Canberra: Department of Health and Aged Care, 157-161

Other published articles

(Invited articles, discussion, and letters to the editor)

• Moore R, Yelland M, Ng S (2012) Moving with the times: familiarity versus formality in Australian general practice. Response to letter to editor Australian Family Physician. 41(1/2). 11.
• McLachlan GJ, Wang K, Ng SK (2008) Contribution to the discussion of the paper by F Chiaromonte and S Tyekucheva. Test, 17, 43-46.
• Ng SK (2005) Incremental scheme of the EM algorithm. In Proceedings of the 55th Session of the International Statistical Institute, Paper #396. The International Statistical Institution.