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Items: 1 to 20 of 93

1.

Identification of gene-environment interactions in cancer studies using penalization.

Liu J, Huang J, Zhang Y, Lan Q, Rothman N, Zheng T, Ma S.

Genomics. 2013 Oct;102(4):189-94. doi: 10.1016/j.ygeno.2013.08.006.

2.

Integrative analysis of prognosis data on multiple cancer subtypes.

Liu J, Huang J, Zhang Y, Lan Q, Rothman N, Zheng T, Ma S.

Biometrics. 2014 Sep;70(3):480-8. doi: 10.1111/biom.12177.

3.

Identifying gene-environment and gene-gene interactions using a progressive penalization approach.

Zhu R, Zhao H, Ma S.

Genet Epidemiol. 2014 May;38(4):353-68. doi: 10.1002/gepi.21807.

4.

A penalized robust semiparametric approach for gene-environment interactions.

Wu C, Shi X, Cui Y, Ma S.

Stat Med. 2015 Dec 30;34(30):4016-30. doi: 10.1002/sim.6609.

PMID:
26239060
5.

A penalized robust method for identifying gene-environment interactions.

Shi X, Liu J, Huang J, Zhou Y, Xie Y, Ma S.

Genet Epidemiol. 2014 Apr;38(3):220-30. doi: 10.1002/gepi.21795.

7.

Integrative analysis of cancer prognosis data with multiple subtypes using regularized gradient descent.

Ma S, Zhang Y, Huang J, Huang Y, Lan Q, Rothman N, Zheng T.

Genet Epidemiol. 2012 Dec;36(8):829-38. doi: 10.1002/gepi.21669.

8.

Integrative analysis of high-throughput cancer studies with contrasted penalization.

Shi X, Liu J, Huang J, Zhou Y, Shia B, Ma S.

Genet Epidemiol. 2014 Feb;38(2):144-51. doi: 10.1002/gepi.21781.

9.
10.

Incorporating network structure in integrative analysis of cancer prognosis data.

Liu J, Huang J, Ma S.

Genet Epidemiol. 2013 Feb;37(2):173-83. doi: 10.1002/gepi.21697.

11.

Analysis of genome-wide association studies with multiple outcomes using penalization.

Liu J, Huang J, Ma S.

PLoS One. 2012;7(12):e51198. doi: 10.1371/journal.pone.0051198.

12.

Identification of non-Hodgkin's lymphoma prognosis signatures using the CTGDR method.

Ma S, Zhang Y, Huang J, Han X, Holford T, Lan Q, Rothman N, Boyle P, Zheng T.

Bioinformatics. 2010 Jan 1;26(1):15-21. doi: 10.1093/bioinformatics/btp604.

13.

Test for interactions between a genetic marker set and environment in generalized linear models.

Lin X, Lee S, Christiani DC, Lin X.

Biostatistics. 2013 Sep;14(4):667-81. doi: 10.1093/biostatistics/kxt006.

14.

Identification of Breast Cancer Prognosis Markers via Integrative Analysis.

Ma S, Dai Y, Huang J, Xie Y.

Comput Stat Data Anal. 2012 Sep 1;56(9):2718-2728.

15.

Identification of breast cancer prognosis markers using integrative sparse boosting.

Ma S, Huang J, Xie Y, Yi N.

Methods Inf Med. 2012;51(2):152-61. doi: 10.3414/ME11-02-0019.

16.

Integrative Analysis of Cancer Diagnosis Studies with Composite Penalization.

Liu J, Huang J, Ma S.

Scand Stat Theory Appl. 2014 Mar 1;41(1):87-103.

17.

Part 1. Statistical Learning Methods for the Effects of Multiple Air Pollution Constituents.

Coull BA, Bobb JF, Wellenius GA, Kioumourtzoglou MA, Mittleman MA, Koutrakis P, Godleski JJ.

Res Rep Health Eff Inst. 2015 Jun;(183 Pt 1-2):5-50.

PMID:
26333238
18.

Integrative analysis and variable selection with multiple high-dimensional data sets.

Ma S, Huang J, Song X.

Biostatistics. 2011 Oct;12(4):763-75. doi: 10.1093/biostatistics/kxr004.

19.
20.

Integrative analysis of multiple cancer prognosis studies with gene expression measurements.

Ma S, Huang J, Wei F, Xie Y, Fang K.

Stat Med. 2011 Dec 10;30(28):3361-71. doi: 10.1002/sim.4337.

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