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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 966   66 Cited 72 times in Scopus72 0
Ng, S. K. and McLachlan, G. J. (2003) An EM-based Semi-Parametric Mixture Model Approach to the Regression Analysis of Competing-Risks Data. Statistics In Medicine, 22 7: 1097-1111. doi:10.1002/sim.1371 123   15 Cited 15 times in Scopus15 0
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.2005.07.003 101   8 Cited 10 times in Scopus10 0
Yau, KKW, Lee, AH and Ng, ASK (2002) A zero-augmented gamma mixed model for longitudinal data with many zeros. Australian & New Zealand Journal of Statistics, 44 2: 177-183. doi:10.1111/1467-842X.00220 153   5 Cited 7 times in Scopus7 0
Ni, X, Zhang, W, Huang, KC, Wang, Y, Ng, SK, Mok, SC, Berkowitz, RS and Ng, SW (2004) Characterisation of human kallikrein 6 protease M expression in ovarian cancer. British Journal of Cancer, 91 4: 725-731. doi:10.1038/sj.bjc.6602041 110   40 Cited 40 times in Scopus40 0
McLachlan, G. J., Bean, R. W. and Ng, S.-K. (2008). Clustering. In Keith, J. M. (Ed.), Bioinformatics, volume 2: Structure, function and applications (pp. 423-439) New Jersey, United States: Humana Press. doi:10.1007/978-1-60327-429-6 74   0
McLachlan, G. J., Flack, L. K., Ng, S. K. and Wang, K. (2013). Clustering of gene expression data via normal mixture models. In Andrei Y. Yakovlev, Lev Klebanov and Daniel Gaile (Ed.), Statistical Methods for Microarray Data Analysis: Methods and Protocols (pp. 103-119) New York, NY, United States: Humana Press. doi:10.1007/978-1-60327-337-4_7 34   Cited 0 times in Scopus0 0
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. In: M Boden and T L Bailey, Conferences in Research and Practice in Information Technology. 2006 Workshop on Intelligent Systems for Bioinformatics (WISB, Hobart, Australia, (29-33). 4 December 2006. 51  
McLachlan, Geoffrey J., Wang, K. and Ng, S. K. (2008) Comments on: Augmenting the bootstrap to analyze high dimensional genomic data. Test, 17 1: 43-46. doi:10.1007/s11749-008-0106-x 75   0 Cited 0 times in Scopus0 0
Lee, A. H., Ng, A.S. K. and Yau, K. K. (2001) Determinants of maternity length of stay: A gamma mixture risk-adjusted model. Health Care Management Science, 4 4: 249-255. 47  
Yau, K. K. W., Ng, S. K., Cheung, M. T. and Tung, M. C. (1999) Estimation of surgeon effects in the analysis of post-operative colorectal cancer patients data. Journal of Applied Statistics, 26 2: 257-272. doi:10.1080/02664769922593 29   2 Cited 2 times in Scopus2 0
Ng, S. K. and McLachlan, G. J. (2007) Extension of Mixture-of-Experts Networks for Binary Classification of Hierarchical Data. Artificial Intelligence in Medicine, 41 1: 57-67. doi:10.1016/j.artmed.2007.06.001 68 3 6 Cited 6 times in Scopus6 0
Yau, KKW, Lee, AH and Ng, ASK (2003) Finite mixture regression model with random effects: application to neonatal hospital length of stay. Computational Statistics & Data Analysis, 41 3-4: 359-366. doi:10.1016/S0167-9473(02)00180-9 144   19 Cited 23 times in Scopus23 0
Jones, L., Ng, A.S. K., Monico, K. A. and McLachlan, G. J. (2004). Linking gene-expression experiments with survival-time data. In: A. Biggeri and et al, Proceedings of the 19th International Workship on Statistical Modelling. 19th International Workshop on Statistical Modelling, Florence, (71-75). 4-8 July 2004. 59  
Yau, KKW and Ng, ASK (2001) Long-term survivor mixture model with random effects: application to a multi-centre clinical trial of carcinoma. Statistics In Medicine, 20 11: 1591-1607. doi:10.1002/sim.932 83   26 Cited 26 times in Scopus26 0
Lee, Andy H., Wang, Kui, Yau, Kelvin K. W., McLachlan, Geoffrey J. and Ng, Shu Kay (2007) Maternity length of stay modelling by Gamma mixture regression with random effects. Biometrical Journal, 49 5: 750-764. doi:10.1002/bimj.200610371 210   6 Cited 6 times in Scopus6 0
Ng, A.S.K. and McLachlan, G. J. (2005). Mixture Model-based Statistical Pattern Recognition of Clustered or Longitudinal Data. In: Brian Lovell and Anthony Maeder, Proceedings of WDIC2005. WDIC2005, Griffith University, (139-144). 21 February 2005. 54 2
Ng, SK, Yau, KKW and Lee, AH (2003) Modelling inpatient length of stay by a hierarchical mixture regression via the EM algorithm. Mathematical And Computer Modelling, 37 3-4: 365-375. doi:10.1016/S0895-7177(03)00012-8 91   2 Cited 4 times in Scopus4 0
Ng, Shu-Kay, Wang, Kui and McLachlan, Geoffrey J. (2006). Multilevel modelling for inference of genetic regulatory networks. In: Axel Bender, Complex Systems, Brisbane, Australia, (S390-S390). 11-14 December 2005. doi:10.1117/12.638449 98   0 Cited 0 times in Scopus0 0
Ng, A. S. K. and McLachlan, G. J. (2005). Normalized Gaussian Networks with Mixed Feature Data. In: S. Zhang and R. Jarvis, AI 2005: Advances in Artificial Intelligence. 18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, (879-882). 5-9 Dec 2005. doi:10.1007/11589990_101 62   2 Cited 1 times in Scopus1 0
McLachlan, G. J., Ng, A.S. K. and Peel, D. (2003). On clustering by mixture models. In M. Schwaiger and O. Opitz (Ed.), Exploratory Data Analysis in Empirical Research (pp. 141-148) Germany: Springer. 42  
Ng, A.S. K. and McLachlan, G. J. (2003) On some variants of the EM algorithm for the fitting of finite mixture models. Austrian Journal of Statistics, 32 1 & 2: 143-161. 50  
Ng, A.S. K. and McLachlan, G. J. (2002). On speeding up the EM algorithm in pattern recognition: A comparison of incremental and multiresolution KD -tree-based approaches. In: D. Suter and A. Bab-Hadiashar, Digital Image Computing Techniques and Applications. Proc. of the Sixth Digital Image Computing Techniques & Applications, Melbourne University, (116-121). 21-22 January. 59  
Ng, S. K. and McLachlan, G. J. (2003) On the Choice of the Number of Blocks with the Incremental EM Algorithm for the Fitting of Normal Mixtures. Statistics And Computing, 13 1: 45-55. doi:10.1023/A:1021987710829 59   8 Cited 10 times in Scopus10 0
Lee, A.H., Xiao, J., Codde, J. P. and Ng, A.S. K. (2002) Public versus private hospital maternity length of stay: A gamma mixture modelling approach. Health Services Management Research, 15 1: 46-54. doi:10.1258/0951484021912824 141   Cited 4 times in Scopus4 0
Huang, KC, Rao, PH, Lau, CC, Heard, E, Ng, SK, Brown, C, Mok, SC, Berkowitz, RS and Ng, SW (2002) Relationship of XIST expression and responses of ovarian cancer to chemotherapy. Molecular Cancer Therapeutics, 1 10: 769-776. 59   24
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. 62  
Ng, A. S. K. and McLachlan, G. J. (2003). Robust estimation in Gaussian mixtures using multiresolution Kd -trees. In: C. Sun, H. Talbot, S. Ourselin and T. Adriaansen, Proceedings of the Seventh International Conference on Digital Image Computing: Techniques and Applications, DICTA 2003. Seventh International Conference on Digital Image Computing: Techniques and Applications, DICTA 2003, Sydney, Australia, (145-154). 10-12 December 2003. 54  
Kim, S-G., Ng, A.S. K., McLachlan, G. J. and Wang, D. (2003). Segmentation of brain MR images with bias field correction. In: B.C. Lovell, Proceedings of the 2003 APRS Workshop on Digital Image Computing. WDIC 2003, The University of Queensland, Brisbane, (3-8). 7 February 2003. 48  
Huang, KC, Park, DC, Ng, SK, Lee, JY, Ni, XY, Ng, WC, Bandera, CA, Welch, WR, Berkowitz, RS, Mok, SC and Ng, SW (2006) Selenium binding protein 1 in ovarian cancer. International Journal of Cancer, 118 10: 2433-2440. doi:10.1002/ijc.21671 62   45 Cited 49 times in Scopus49 0
Ng, Shu-Kay and McLachlan, Geoffrey J. (2004) Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images. Pattern Recognition, 37 8: 1573-1589. doi:10.1016/j.patcog.2004.02.012 80   15 Cited 16 times in Scopus16 0
Ng, S. K., Krishnan, T. and McLachlan, G. J. (2004). The EM algorithm. In J.E. Gentle, W. Hardle and Y. Mori (Ed.), Handbook of Computational Statistics: Concepts and Methods (pp. 137-168) Germany: Springer-Verlag. 81  
Ehrlich, Joshua R., Tang, Liangdan, Caiazzo Jr., Robert J., Cramer, Daniel W., Ng, Shu-Kay, Ng, Shu-Wing and Liu, Brian C.-S. (2008) The "reverse capture" autoantibody microarray: An innovative approach to profiling the autoantibody response to tissue-derived native antigens. Methods in Molecular Biology, 441 175-192. doi:10.1007/978-1-60327-047-2_12 10   Cited 11 times in Scopus11 0
Dunn, Jeff, Ng, Shu Kay, Holland, Jimmie, Aitken, Joanne, Youl, Pip, Baade, Peter D. and Chambers, Suzanne K. (2013) Trajectories of psychological distress after colorectal cancer. Psycho-Oncology, 22 8: 1759-1765. doi:10.1002/pon.3210 52   3 Cited 8 times in Scopus8 0
Ng, S. K. and McLachlan, G. J. (2004) Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification. Ieee Transactions On Neural Networks, 15 3: 738-749. doi:10.1109/TNN.2004.826217 80   17 Cited 20 times in Scopus20 0