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Items: 1 to 20 of 61

1.

Partial identification in the statistical matching problem.

Ahfock D, Pyne S, Lee SX, McLachlan GJ.

Comput Stat Data Anal. 2016 Dec;104:79-90. doi: 10.1016/j.csda.2016.06.005.

2.

Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data.

Nguyen HD, McLachlan GJ, Orban P, Bellec P, Janke AL.

Neural Comput. 2017 Apr;29(4):990-1020. doi: 10.1162/NECO_a_00938. Epub 2017 Jan 17.

PMID:
28095191
3.

Statistical Evaluation of Labeled Comparative Profiling Proteomics Experiments Using Permutation Test.

Nguyen HD, McLachlan GJ, Hill MM.

Methods Mol Biol. 2017;1549:109-117.

PMID:
27975287
4.

Clustering.

McLachlan GJ, Bean RW, Ng SK.

Methods Mol Biol. 2017;1526:345-362.

PMID:
27896751
5.

A Universal Approximation Theorem for Mixture-of-Experts Models.

Nguyen HD, Lloyd-Jones LR, McLachlan GJ.

Neural Comput. 2016 Dec;28(12):2585-2593. Epub 2016 Sep 14.

PMID:
27626962
6.

Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data.

Lloyd-Jones LR, Nguyen HD, McLachlan GJ, Sumpton W, Wang YG.

Biometrics. 2016 Dec;72(4):1255-1265. doi: 10.1111/biom.12531. Epub 2016 Apr 28.

PMID:
27123964
7.

Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data.

Tian T, McLachlan GJ, Dieters MJ, Basford KE.

PLoS One. 2015 Dec 21;10(12):e0144370. doi: 10.1371/journal.pone.0144370. eCollection 2015.

8.

Modeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedure.

Lee SX, McLachlan GJ, Pyne S.

Cytometry A. 2016 Jan;89(1):30-43. doi: 10.1002/cyto.a.22789. Epub 2015 Oct 22.

9.

A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes.

Aghaeepour N, Chattopadhyay P, Chikina M, Dhaene T, Van Gassen S, Kursa M, Lambrecht BN, Malek M, McLachlan GJ, Qian Y, Qiu P, Saeys Y, Stanton R, Tong D, Vens C, Walkowiak S, Wang K, Finak G, Gottardo R, Mosmann T, Nolan GP, Scheuermann RH, Brinkman RR.

Cytometry A. 2016 Jan;89(1):16-21. doi: 10.1002/cyto.a.22732. Epub 2015 Oct 8.

10.

Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data.

Pyne S, Lee SX, Wang K, Irish J, Tamayo P, Nazaire MD, Duong T, Ng SK, Hafler D, Levy R, Nolan GP, Mesirov J, McLachlan GJ.

PLoS One. 2014 Jul 1;9(7):e100334. doi: 10.1371/journal.pone.0100334. eCollection 2014.

11.

Inference on differences between classes using cluster-specific contrasts of mixed effects.

Ng SK, McLachlan GJ, Wang K, Nagymanyoki Z, Liu S, Ng SW.

Biostatistics. 2015 Jan;16(1):98-112. doi: 10.1093/biostatistics/kxu028. Epub 2014 Jun 23.

PMID:
24963011
12.

False discovery rate control in magnetic resonance imaging studies via Markov random fields.

Nguyen HD, McLachlan GJ, Cherbuin N, Janke AL.

IEEE Trans Med Imaging. 2014 Aug;33(8):1735-48. doi: 10.1109/TMI.2014.2322369. Epub 2014 May 7.

PMID:
24816549
13.

Critical assessment of automated flow cytometry data analysis techniques.

Aghaeepour N, Finak G; FlowCAP Consortium; DREAM Consortium, Hoos H, Mosmann TR, Brinkman R, Gottardo R, Scheuermann RH.

Nat Methods. 2013 Mar;10(3):228-38. doi: 10.1038/nmeth.2365. Epub 2013 Feb 10. Erratum in: Nat Methods. 2013 May;10(5):445.

14.

Clustering of gene expression data via normal mixture models.

McLachlan GJ, Flack LK, Ng SK, Wang K.

Methods Mol Biol. 2013;972:103-19. doi: 10.1007/978-1-60327-337-4_7.

PMID:
23385534
15.

Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects.

Wang K, Ng SK, McLachlan GJ.

BMC Bioinformatics. 2012 Nov 14;13:300. doi: 10.1186/1471-2105-13-300.

16.

On the classification of microarray gene-expression data.

Basford KE, McLachlan GJ, Rathnayake SI.

Brief Bioinform. 2013 Jul;14(4):402-10. doi: 10.1093/bib/bbs056. Epub 2012 Sep 17.

PMID:
22988257
17.

Conservation and divergence in Toll-like receptor 4-regulated gene expression in primary human versus mouse macrophages.

Schroder K, Irvine KM, Taylor MS, Bokil NJ, Le Cao KA, Masterman KA, Labzin LI, Semple CA, Kapetanovic R, Fairbairn L, Akalin A, Faulkner GJ, Baillie JK, Gongora M, Daub CO, Kawaji H, McLachlan GJ, Goldman N, Grimmond SM, Carninci P, Suzuki H, Hayashizaki Y, Lenhard B, Hume DA, Sweet MJ.

Proc Natl Acad Sci U S A. 2012 Apr 17;109(16):E944-53. doi: 10.1073/pnas.1110156109. Epub 2012 Mar 26.

18.

Testing for group structure in high-dimensional data.

McLachlan GJ, Rathnayake SI.

J Biopharm Stat. 2011 Nov;21(6):1113-25. doi: 10.1080/10543406.2011.608342.

PMID:
22023680
19.

Commentary on Steinley and Brusco (2011): recommendations and cautions.

McLachlan GJ.

Psychol Methods. 2011 Mar;16(1):80-1; discussion 89-92. doi: 10.1037/a0021141.

PMID:
21381818
20.

Mixtures of common t-factor analyzers for clustering high-dimensional microarray data.

Baek J, McLachlan GJ.

Bioinformatics. 2011 May 1;27(9):1269-76. doi: 10.1093/bioinformatics/btr112. Epub 2011 Mar 3.

PMID:
21372081

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