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Items: 1 to 20 of 41

1.

Training in metabolomics research. I. Designing the experiment, collecting and extracting samples and generating metabolomics data.

Barnes S, Benton HP, Casazza K, Cooper SJ, Cui X, Du X, Engler J, Kabarowski JH, Li S, Pathmasiri W, Prasain JK, Renfrow MB, Tiwari HK.

J Mass Spectrom. 2016 Jul;51(7):461-75. doi: 10.1002/jms.3782.

2.

Inferential considerations for low-count RNA-seq transcripts: a case study on the dominant prairie grass Andropogon gerardii.

Raithel S, Johnson L, Galliart M, Brown S, Shelton J, Herndon N, Bello NM.

BMC Genomics. 2016 Feb 27;17:140. doi: 10.1186/s12864-016-2442-7.

3.

Assessing Dissimilarity Measures for Sample-Based Hierarchical Clustering of RNA Sequencing Data Using Plasmode Datasets.

Reeb PD, Bramardi SJ, Steibel JP.

PLoS One. 2015 Jul 10;10(7):e0132310. doi: 10.1371/journal.pone.0132310. eCollection 2015.

4.

From Measurement to Analysis Reporting: Grand Challenges in Nutritional Methodology.

Mehta T, Allison DB.

Front Nutr. 2014;1(6). pii: 00006. No abstract available.

5.

Coverage recommendations for methylation analysis by whole-genome bisulfite sequencing.

Ziller MJ, Hansen KD, Meissner A, Aryee MJ.

Nat Methods. 2015 Mar;12(3):230-2, 1 p following 232. doi: 10.1038/nmeth.3152. Epub 2014 Nov 2.

6.

On the impoverishment of scientific education.

Dougherty ER.

EURASIP J Bioinform Syst Biol. 2013 Nov 11;2013(1):15. doi: 10.1186/1687-4153-2013-15.

7.

Evaluating statistical analysis models for RNA sequencing experiments.

Reeb PD, Steibel JP.

Front Genet. 2013 Sep 17;4:178. doi: 10.3389/fgene.2013.00178. eCollection 2013.

8.

Amniotic fluid: the use of high-dimensional biology to understand fetal well-being.

Kamath-Rayne BD, Smith HC, Muglia LJ, Morrow AL.

Reprod Sci. 2014 Jan;21(1):6-19. doi: 10.1177/1933719113485292. Epub 2013 Apr 18. Review.

9.

A methodology to assess the intrinsic discriminative ability of a distance function and its interplay with clustering algorithms for microarray data analysis.

Giancarlo R, Lo Bosco G, Pinello L, Utro F.

BMC Bioinformatics. 2013;14 Suppl 1:S6. doi: 10.1186/1471-2105-14-S1-S6. Epub 2013 Jan 14.

10.

Industrial methodology for process verification in research (IMPROVER): toward systems biology verification.

Meyer P, Hoeng J, Rice JJ, Norel R, Sprengel J, Stolle K, Bonk T, Corthesy S, Royyuru A, Peitsch MC, Stolovitzky G.

Bioinformatics. 2012 May 1;28(9):1193-201. doi: 10.1093/bioinformatics/bts116. Epub 2012 Mar 14. Review.

11.

The illusion of distribution-free small-sample classification in genomics.

Dougherty ER, Zollanvari A, Braga-Neto UM.

Curr Genomics. 2011 Aug;12(5):333-41. doi: 10.2174/138920211796429763.

12.
13.

Multiple-rule bias in the comparison of classification rules.

Yousefi MR, Hua J, Dougherty ER.

Bioinformatics. 2011 Jun 15;27(12):1675-83. doi: 10.1093/bioinformatics/btr262. Epub 2011 May 5.

14.

Comparing self-reported ethnicity to genetic background measures in the context of the Multi-Ethnic Study of Atherosclerosis (MESA).

Divers J, Redden DT, Rice KM, Vaughan LK, Padilla MA, Allison DB, Bluemke DA, Young HJ, Arnett DK.

BMC Genet. 2011 Mar 4;12:28. doi: 10.1186/1471-2156-12-28.

15.

A comprehensive and universal method for assessing the performance of differential gene expression analyses.

Dozmorov MG, Guthridge JM, Hurst RE, Dozmorov IM.

PLoS One. 2010 Sep 9;5(9). pii: e12657. doi: 10.1371/journal.pone.0012657.

16.

Forward-time simulation of realistic samples for genome-wide association studies.

Peng B, Amos CI.

BMC Bioinformatics. 2010 Sep 1;11:442. doi: 10.1186/1471-2105-11-442.

17.

Within-Cluster Resampling for Analysis of Family Data: Ready for Prime-Time?

Tiwari HK, Patki A, Allison DB.

Stat Interface. 2010 Apr 1;3(2):169-176.

18.

Metabolomics in premature labor: a novel approach to identify patients at risk for preterm delivery.

Romero R, Mazaki-Tovi S, Vaisbuch E, Kusanovic JP, Chaiworapongsa T, Gomez R, Nien JK, Yoon BH, Mazor M, Luo J, Banks D, Ryals J, Beecher C.

J Matern Fetal Neonatal Med. 2010 Dec;23(12):1344-59. doi: 10.3109/14767058.2010.482618. Epub 2010 May 26.

19.
20.

The use of plasmodes as a supplement to simulations: A simple example evaluating individual admixture estimation methodologies.

Vaughan LK, Divers J, Padilla M, Redden DT, Tiwari HK, Pomp D, Allison DB.

Comput Stat Data Anal. 2009 Mar 15;53(5):1755-1766.

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