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Your Environment. Your Health.

Pierre R. Bushel, Ph.D.

Biostatistics & Computational Biology Branch

Pierre R. Bushel, Ph.D.
Pierre R. Bushel, Ph.D.
Staff Scientist
Tel 984-287-3714
Fax 301-480-3569
P.O. Box 12233
Mail Drop A3-03
Durham, N.C. 27709

Research Summary

Pierre Bushel, Ph.D., and his staff use their expertise in bioinformatics to design analytical methodologies for genomic data analysis and develop databases for environmental informatics. Their research focuses on integrating phenotypic observations, end-point measurements and associated biological information with expression and genomics data for a better understanding of the biological mechanisms and pathways that are perturbed by stressors. The group has several ongoing projects:

  • Integrating phenotypic observations, end-point measurements and associated biological information with expression and genomic data for better interpretation of the biological mechanisms and pathways that are perturbed by stressors
  • Specialized phenotypic clustering and statistical model building for classification and prediction
  • Gene expression data mining, biological theme extraction from expression data and biomedical literature
  • Toxicogenomics and environmental health sciences
  • eQTL analysis
  • Association of genome characteristics and perturbed expression patterns

In addition, Bushel and his group provide bioinformatics, data analysis, computational biology and statistical genetics/genomics support as well as software, database and application development expertise to NIEHS Division of Intramural Research investigators at no cost.


  • Extracting Patterns and Identifying co-expressed Genes Sequence (EPIG-Seq)
    A desktop software for the analysis of RNA-Seq data to extract patterns and identify co-expressed genes.
  • Expression Predictor (ExP)
    ("/Rhythmyx/assembler/render?sys_contentid=34881&sys_revision=4&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="34881" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="34881" sys_siteid="" sys_folderid="")A desktop application developed with Java programming language for classifying and predicting samples based on gene expression data using a simplified fuzzy adaptive resonance theory map (SFAM) neural network architecture.
  • Modk-Prototypes
    ("/Rhythmyx/assembler/render?sys_contentid=35062&sys_revision=4&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35062" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35062" sys_siteid="" sys_folderid="")Clusters biological samples by simultaneously considering microarray gene expression data and classes of known phenotypic variables such as clinical chemistry evaluations and histopathologic observations.
  • PCA-based Gene Filtering
    ("/Rhythmyx/assembler/render?sys_contentid=35076&sys_revision=2&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35076" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35076" sys_siteid="" sys_folderid="")A new filtering statistic for Affymetrix GeneChips, based on principle component analysis (PCA) on the probe-level gene expression data.
  • Phase-shifted Analysis of Gene Expression (PAGE)
    ("/Rhythmyx/assembler/render?sys_contentid=35110&sys_revision=2&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35110" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35110" sys_siteid="" sys_folderid="")An interactive tool that uses a line graph to dynamically illustrate the phase-shifted patterns of gene expressions based on the q-Cluster selected by the users.
  • Principal Variance Component Analysis ("/Rhythmyx/assembler/render?sys_contentid=35299&sys_revision=3&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35299" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35299" sys_siteid="" sys_folderid="")
    A hybrid approach using principal components analysis and variance component analysis as a methodology to determine and quantify sources of variability most prominent in microarray gene expression data.
  • SA-Modk-Prototypes
    ("/Rhythmyx/assembler/render?sys_contentid=35617&sys_revision=2&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35617" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35617" sys_siteid="" sys_folderid="")For simultaneous clustering of gene expression data with clinical chemistry and pathological evaluations using simulated annealing.
  • Sparse Multiclass SVM: Multiclass SVM with Variable Selection (SMS)
    Support vector machines (SVMs) have shown superior performance in cancer classification due to their ability to handle high dimensional low sample size data. Multiclass support vector machines (MSVMs) provide a natural framework for multi-class learning. Despite its effective performance, the procedure utilizes all variables without selection. Spare MC-SVM (SMS) improves the procedure by imposing shrinkage penalties in learning to enforce solution sparsity.
  • Systematic Variation Normalization (SVN)
    ("/Rhythmyx/assembler/render?sys_contentid=35622&sys_revision=3&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="35622" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="35622" sys_siteid="" sys_folderid="")A procedure for removing systematic variation in microarray gene expression data.

Selected Publications

  1. Mav D, Shah RR, Howard BE, Auerbach SS, Bushel PR, Collins JB, Gerhold DL, Judson RS, Karmaus AL, Maull EA, Mendrick DL, Merrick BA, Sipes NS, Svoboda D, Paules RS. A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics. PLoS One. 2018 Feb 20;13(2):e0191105. [Abstract] 
  2. Duncan CG, Kondilis-Mangum HD, Grimm SA, Bushel PR, Chrysovergis K, Roberts JD, Tyson FL, Merrick BA, Wade PA. Base-Resolution Analysis of DNA Methylation Patterns Downstream of Dnmt3a in Mouse Naïve B Cells. G3 (Bethesda). 2018 Mar 2;8(3):805-813. [Abstract] 
  3. Funderburk KM, Auerbach SS, Bushel PR. Crosstalk between Receptor and Non-receptor Mediated Chemical Modes of Action in Rat Livers Converges through a Dysregulated Gene Expression Network at Tumor Suppressor Tp53. Front Genet. 2017 Oct 24;8:157. [Abstract] 
  4. Bennett BD, Bushel PR. goSTAG: gene ontology subtrees to tag and annotate genes within a set. Source Code Biol Med. 2017 Apr 13;12:6. [Abstract] 
  5. Manrai AK, Cui Y, Bushel PR, Hall M, Karakitsios S, Mattingly CJ, Ritchie M, Schmitt C, Sarigiannis DA, Thomas DC, Wishart D, Balshaw DM, Patel CJ. Informatics and Data Analytics to Support Exposome-Based Discovery for Public Health. Annu Rev Public Health. 2017 Mar 20;38:279-294. [Abstract] 
  6. Osgood RS, Upham BL, Bushel PR, Velmurugan K, Xiong K, Bauer AK. Secondhand Smoke-Prevalent Polycyclic Aromatic Hydrocarbon Binary Mixture-Induced Specific Mitogenic and Pro-inflammatory Cell Signaling Events in Lung Epithelial Cells. Toxicol Sci. 2017 May 1;157(1):156-171. [Abstract] 
  7. Bushel PR, Fannin RD, Gerrish K, Watkins PB, Paules RS. Blood gene expression profiling of an early acetaminophen response. Pharmacogenomics J. 2017 Jun;17(3):230-236. [Abstract]
  8. Smith A, Calley J, Mathur S, Qian HR, Wu H, Farmen M, Caiment F, Bushel PR, Li J, Fisher C, Kirby P, Koenig E, Hall DG, Watson DE. The Rat microRNA body atlas; Evaluation of the microRNA content of rat organs through deep sequencing and characterization of pancreas enriched miRNAs as biomarkers of pancreatic toxicity in the rat and dog. BMC Genomics. 2016 Aug 30;17:694. [Abstract]
  9. Kim SH, Trinh AT, Larsen MC, Mastrocola AS, Jefcoate CR, Bushel PR, Tibbetts RS. Tunable regulation of CREB DNA binding activity couples genotoxic stress response and metabolism. Nucleic Acids Res. 2016 Jul 18. pii: gkw643. [Epub ahead of print] [Abstract]
  10. Ciencewicki JM, Verhein KC, Gerrish K, McCaw ZR, Li J, Bushel PR, Kleeberger SR. Effects of mannose-binding lectin on pulmonary gene expression and innate immune inflammatory response to ozone. Am J Physiol Lung Cell Mol Physiol. 2016 Aug 1;311(2):L280-91. [Abstract]
  11. Li J, Bushel PR. EPIG-Seq: extracting patterns and identifying co-expressed genes from RNA-Seq data. BMC Genomics. 2016 Mar 22;17:255. [Abstract]
  12. Fannin RD, Gerrish K, Sieber SO, Bushel PR, Watkins PB, Paules RS. Blood transcript immune signatures distinguish a subset of people with elevated serum ALT from others given acetaminophen. Clinical pharmacology and therapeutics 2016 99(4):432-441. [Abstract]
  13. Bourdon-Lacombe JA, Moffat ID, Deveau M, Husain M, Auerbach S, Krewski D, Thomas RS, Bushel PR, Williams A, Yauk CL. Technical guide for applications of gene expression profiling in human health risk assessment of environmental chemicals. Regul Toxicol Pharmacol. 2015 Jul;72(2):292-309. Epub 2015 May 2. [Abstract]
  14. Verhein KC, McCaw Z, Gladwell W, Trivedi S, Bushel PR, Kleeberger SR. Novel Roles for Notch3 and Notch4 Receptors in Gene Expression and Susceptibility to Ozone-Induced Lung Inflammation in Mice. Environ Health Perspect. 2015 Aug;123(8):799-805. Epub 2015 Feb 6. [Abstract]
  15. Yang J, Bennett BD, Luo S, Inoue K, Grimm SA, Schroth GP, Bushel PR, Kinyamu HK, Archer TK. LIN28A Modulates Splicing and Gene Expression Programs in Breast Cancer Cells. Molecular and cellular biology 2015 35(18):3225-3243. [Abstract]
  16. Hewitt SC, Winuthayanon W, Pockette B, Kerns RT, Foley JF, Flagler N, Ney E, Suksamrarn A, Piyachaturawat P, Bushel PR, Korach KS. Development of phenotypic and transcriptional biomarkers to evaluate relative activity of potentially estrogenic chemicals in ovariectomized mice. Environmental health perspectives 2015 123(4):344-352. [Abstract]
  17. Davis M, Li J, Knight E, Eldridge SR, Daniels KK, Bushel PR. Toxicogenomics profiling of bone marrow from rats treated with topotecan in combination with oxaliplatin: a mechanistic strategy to inform combination toxicity. Frontiers in genetics 2015 6():14-. [Abstract]
  18. SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nature Biotechnology 2014 32(9):903-914. [Abstract]
  19. Wang C, Gong B, Bushel PR, Thierry-Mieg J, Thierry-Mieg D, Xu J, Fang H, Hong H, Shen J, Su Z, Meehan J, Li X, Yang L, Li H, Łabaj PP, Kreil DP, Megherbi D, Gaj S, Caiment F, van Delft J, Kleinjans J, Scherer A, Devanarayan V, Wang J, Yang Y, Qian HR, Lancashire LJ, Bessarabova M, Nikolsky Y, Furlanello C, Chierici M, Albanese D, Jurman G, Riccadonna S, Filosi M, Visintainer R, Zhang KK, Li J, Hsieh JH, Svoboda DL, Fuscoe JC, Deng Y, Shi L, Paules RS, Auerbach SS, Tong W5. The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nature Biotechnology 2014 32(9):926-932. [Abstract]
  20. Madenspacher JH, Azzam KM, Gowdy KM, Malcolm KC, Nick JA, Dixon D, Aloor JJ, Draper DW, Guardiola JJ, Shatz M, Menendez D, Lowe J, Lu J, Bushel P, Li L, Merrick BA, Resnick MA, Fessler MB. p53 Integrates host defense and cell fate during bacterial pneumonia.  Journal of Experimental Medicine 2013 210(5):891-904. [Abstract]
  21. Zhang L, Simpson DA, Innes CL, Chou J, Bushel PR, Paules RS, Kaufmann WK, Zhou T. Gene expression signatures but not cell cycle checkpoint functions distinguish AT carriers from normal individuals. Physiological genomics 2013 45(19):907-916.
  22. Williams-Devane CR, Reif DM, Cohen Hubal E, Bushel PR, Hudgens EE, Gallagher JE, Edwards SW. Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes. BMC Syst Biol. 2013 Nov 4;7(1):119
  23. Huda A, Bushel PR. Widespread Exonization of Transposable Elements in Human Coding Sequences is Associated with Epigenetic Regulation of Transcription. Transcriptomics: Open Access 2013 1(1):1000101-. [Abstract]
  24. Huang L, Zhang HH, Zeng ZB, Bushel PR. Improved Sparse Multi-Class SVM and Its Application for Gene Selection in Cancer Classification. Cancer informatics 12:143-153, 2013. [Abstract]
  25. Lu J, Bushel PR. Dynamic expression of 3' UTRs revealed by Poisson hidden Markov modeling of RNA-Seq: Implications in gene expression profiling. Gene 527(2):616-623, 2013. [Abstract]
  26. Davis B, Risinger J , Chandramouli G, Pierre P, Baird D, Peddada S. Gene Expression in Uterine Leiomyoma from Tumors Likely to Be Growing (from Black Women over 35) and Tumors Likely to Be Non-Growing (from White Women over 35). PloS one 8(6):e63909, 2013. [Abstract]
  27. Arana ME, Kerns RT, Wharey L, Gerrish KE, Bushel PR, Kunkel TA. Transcriptional responses to loss of RNase H2 in Saccharomyces cerevisiae. DNA repair. 11(12):933-41, 2012. [Abstract]
  28. Zhang, L., Bushel, P.R., Chou, J., Zhou, T. & Watkins, P.B. Identification of identical transcript changes in liver and whole blood during acetaminophen toxicity. Front Genet, 2012.
  29. Pandiri AR, Sill RC, Ziglioli V, Ton TV, Hong HH, Lahousse SA, Gerrish KE, Auerbach SS, Shockley KR, Bushel PR, Peddada SD, Hoenerhoff MJ. (2012). Differential transcriptomic analysis of spontaneous lung tumors in B6C3F1 Mice: comparison to human non-small cell lung cancer. Toxicologic Pathology 40(8):1141-1159. [Abstract]
  30. Bushel PR, McGovern R, Liu L, Hofmann O, Huda A, Lu J, Hide W, Lin X. Population differences in transcript-regulator expression quantitative trait loci. PLoS One. 2012;7(3):e34286. Epub 2012 Mar 27.
  31. Hewitt SC, Li L, Grimm SA, Chen Y, Liu L, Li Y, Bushel PR, Fargo D, Korach KS. Research resource: whole-genome estrogen receptor α binding in mouse uterine tissue revealed by ChIP-seq. Mol Endocrinol. 2012 May;26(5):887-98. Epub 2012 Mar 22.
  32. Chang C, Wang J, Zhao C, Fostel J, Tong W, Bushel PR, Deng Y, Pusztai L, Symmans WF, Shi T. Maximizing biomarker discovery by minimizing gene signatures. BMC Genomics. 2011 Dec 23;12 Suppl 5:S6. Epub 2011 Dec 23.
  33. Kerns RT, Bushel PR. The impact of classification of interest on predictive toxicogenomics. Front Genet. 2012;3:14. Epub 2012 Feb 7.
  34. Corton JC, Bushel PR, Fostel J, O'Lone RB. Sources of variance in baseline gene expression in the rodent liver. Mutat Res. 2012 Aug 15;746(2):104-12. Epub 2012 Jan 5.
  35. Hoenerhoff MJ, Pandiri AR, Lahousse SA, Hong HH, Ton TV, Auerbach SS, Gerrish K, Bushel PR, Shockley KR, Peddada SD, Sills RC. Global gene expression profiling of spontaneous hepatocellular carcinoma in B6C3F1 mice: similarities in the molecular landscape with human liver cancer. Toxicologic Pathology 2011 39(4):678-699. [Abstract]  
  36. Lu J, Kerns RT, Peddada S, Bushel PR. Principal component analysis-based filtering improves detection for Affymetrix gene expression arrays. Nucleic Acids Research 39(13):e86, 2011. [Abstract]
  37. Eggesb M, Moen B, Peddada S, Baird D, Rugtveit J, Midtvedt T, Bushel PR, Sekelja M and Rudi K. Development of gut microbiota in infants not exposed to medical interventions. APMIS Jan;119(1):17-35, 2011 [Abstract]  
  38. Afshari CA, Hamadeh HK, Bushel PR. The evolution of bioinformatics in toxicology: advancing toxicogenomics. Toxicological Sciences Mar;120 Suppl 1:S225-S237, 2011 [Abstract]  
  39. Shi L, Campbell G, Jones WD, Campagne F, Wen Z, Walker SJ, Su Z, Chu TM, Goodsaid FM, Pusztai L, Shaughnessy JD Jr, Oberthuer A, Thomas RS, Paules RS, Fielden M, Barlogie B, Chen W, Du P, Fischer M, Furlanello C, Gallas BD, Ge X, Megherbi DB, Symmans WF, Wang MD, Zhang J, Bitter H, Brors B, Bushel PR, Bylesjo M, Chen M, Cheng J, Cheng J, Chou J, Davison TS, Delorenzi M, Deng Y, Devanarayan V, Dix DJ, Dopazo J, Dorff KC, Elloumi F, Fan J, Fan S, Fan X, Fang H, Gonzaludo N, Hess KR, Hong H, Huan J, Irizarry RA, Judson R, Juraeva D, Lababidi S, Lambert CG, Li L, Li Y, Li Z, Lin SM, Liu G, Lobenhofer EK, Luo J, Luo W, McCall MN, Nikolsky Y, Pennello GA, Perkins RG, Philip R, Popovici V, Price ND, Qian F, Scherer A, Shi T, Shi W, Sung J, Thierry-Mieg D, Thierry-Mieg J, Thodima V, Trygg J, Vishnuvajjala L, Wang SJ, Wu J, Wu Y, Xie Q, Yousef WA, Zhang L, Zhang X, Zhong S, Zhou Y, Zhu S. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nature biotechnology 2010 28(8):827-38.  [Abstract]
  40. Huang J, Shi W, Zhang J, Chou JW, Paules RS, Gerrish K, Li J, Luo J, Wolfinger RD, Bao W, Chu TM, Nikolsky Y, Nikolskaya T, Dosymbekov D, Tsyganova MO, Shi L, Fan X, Corton JC, Chen M, Cheng Y, Tong W, Fang H, Bushel PR. Genomic indicators in the blood predict drug-induced liver injury. Pharmacogenomics J. Aug;10(4):267-77, 2010. [Abstract]  
  41. Bushel PR, Nielsen D, Tong W. Proceedings of the first international conference on Toxicogenomics Integrated with Environmental Sciences (TIES-2007). BMC proceedings 3(suppl 2):S1-, 2009. [Abstract]  
  42. Bushel PR. Clustering of gene expression data and end-point measurements by simulated annealing. Journal of bioinformatics and computational biology 7(1):193-215, 2009. [Abstract]  
  43. Bushel PR, Heard NA, Gutman R, Liu L, Peddada SD, Pyne S. Dissecting the fission yeast regulatory network reveals phase-specific control elements of its cell cycle BMC Systems Biology. Sept 16;3:93, 2009.  
  44. Chou JW, Bushel PR. Discernment of possible mechanisms of hepatotoxicity via biological processes over-represented by co-expressed genes. BMC Genomics. Jun 18;10:272, 2009. [Abstract]  
  45. Lobenhofer EK, Auman JT, Blackshear PE, Boorman GA, Bushel PR, Cunningham ML, Fostel JM, Gerrish K, Heinloth AN, Irwin RD, Malarkey DE, Merrick BA, Sieber SO, Tucker CJ, Ward SM, Wilson RE, Hurban P, Tennant RW, Paules RS. Gene expression response in target organ and whole blood varies as a function of target organ injury phenotype. Genome Biol. 2008 9(6):R100. Epub 2008 June 20. [Abstract]  
  46. Boedigheimer MJ , Wolfinger RD, Bass MB, Bushel PR , Chou JW , Cooper M , Corton JC, Fostel J, Hester S, Lee JS, Liu F , Liu J, Qian H , Quackenbush J, Pettit S, Thompson KL. Sources of variation in baseline gene expression levels from toxicogenomics study control animals across multiple laboratories. BMC genomics 2008 9:285. [Abstract]  
  47. Huang L, Heinloth AN, Zhao-Bang Z, Paules RS, Bushel PR.Genes related to apoptosis predict necrosis of the liver as a phenotype observed in rats exposed to a compendium of hepatotoxicants. BMC Genomics. 2008 Jun 16;9:288. [Abstract]  
  48. Jin YH, Dunlap PE , McBride SJ, Al-Refai H, Bushel PR, Freedman JH. Global transcriptome and deletome profiles of yeast exposed to transition metals. PLoS Genetics 2008 4(4):e1000005-. [Abstract]  
  49. Waters M, Stasiewicz S, Merrick BA, Tomer K, Bushel P, Paules R, Stegman N, Nehls G , Yost KJ, Johnson CH, Gustafson SF, Xirasagar S, Xiao N, Huang C-C, Boyer P, Chan DD, Pan Q, Gong H, Taylor J, Fostel J, Choi D, Rashid A, Ahmed A, Howle R, Selkirk J, Tennant R . CEBS: Chemical Effects in Biological Systems. A public data repository integrating study design and toxicity data with microarray and proteomics data.. Nucleic acids research 2008 36:D892-D900. [Abstract]  
  50. Bushel PR, Heinloth AN, Li J, Huang L, Chou JW, Boorman GA, Malarkey DE, Houle CD, Ward SM, Wilson RE, Fannin RD, Russo MW, Watkins PB, Tennant RW, Paules RS. Blood gene expression signatures predict exposure levels. Proc Natl Acad Sci U S A. 2007 Nov 13;104(46):18211-6. Epub 2007 Nov 2. [Abstract]  
  51. Chou JW, Zhou T, Kaufmann WK, Paules RS, Bushel PR. Extracting gene expression patterns and identifying co-expressed genes from microarray data. BMC bioinformatics 2007 8(1):427-427. [Abstract]  
  52. Zhou T, Chou J, Mullen TE, Elkon R, Zhou Y, Simpson DA, Bushel PR, Paules RS, Lobenhofer EK, Hurban P, Kaufmann WK. Identification of primary transcriptional regulation of cell-cycle-regulated genes upon DNA damage. Cell cycle (Georgetown, Tex.) 2007 8: 972-981 [Abstract]  
  53. Zhou T, Chou J, Zhou Y, Simpson DA, Cao F, Bushel PR, Paules RS, Kaufmann WK. Ataxia telangiectasia-mutated dependent DNA damage checkpoint functions regulate gene expression in human fibroblasts. Mol Cancer Res. 2007 Aug; 5(8): 813-22. [Abstract]  
  54. Fostel JM, Burgoon L, Zwickl C, Lord P, Corton JC, Bushel PR, Cunningham M, Fan L, Edwards SW, Hester S, Stevens J, Tong W, Waters M, Yang C, Tennant R. Toward a checklist for exchange and interpretation of data from a toxicology study. Toxicol Sci. 2007 Sep;99(1):26-34. Epub 2007 Apr 17.  
  55. Bushel PR, Wolfinger RD, Gibson G. Simultaneous clustering of gene expression data with clinical chemistry and pathological evaluations reveals phenotypic prototypes. BMC Syst Biol. 2007 Feb 23;1:15. [Abstract]  
  56. Xirasagar S, Gustafson SF, Huang CC, Pan Q, Fostel J, Boyer P, Merrick BA, Tomer KB, Chan DD, Yost KJ 3rd, Choi D, Xiao N, Stasiewicz S, Bushel P, Waters MD. Chemical effects in biological systems (CEBS) object model for toxicology data, SysTox-OM: design and application. Bioinformatics. 2006 Apr 1;22(7):874-82. Epub 2006 Jan 12. [Abstract]  
  57. Innes CL, Heinloth AN, Flores KG, Sieber SO, Deming PB, Bushel PR, Kaufmann WK, Paules RS. ATM requirement in gene expression responses to ionizing radiation in human lymphoblasts and fibroblasts. Mol Cancer Res. 2006 Mar;4(3):197-207. [Abstract]  
  58. Zhou T, Chou JW, Simpson DA, Zhou Y, Mullen TE, Medeiros M, Bushel PR, Paules RS, Yang X, Hurban P, Lobenhofer EK, Kaufmann WK. Profiles of global gene expression in ionizing-radiation-damaged human diploid fibroblasts reveal synchronization behind the G1 checkpoint in a G0-like state of quiescence. EHP. 2006 Apr;114(4):553-9. [Abstract]  
  59. Fostel J, Choi D, Zwickl C, Morrison N, Rashid A, Hasan A, Bao W, Richard A, Tong W, Bushel PR, Brown R, Bruno M, Cunningham ML, Dix D, Eastin W, Frade C, Garcia A, Heinloth A, Irwin R, Madenspacher J, Merrick BA, Papoian T, Paules R, Rocca-Serra P, Sansone AS, Stevens J, Tomer K, Yang C, Waters M. Chemical effects in biological systems--data dictionary (CEBS-DD): a compendium of terms for the capture and integration of biological study design description, conventional phenotypes, and 'omics data. Toxicol Sci. 2005 Dec;88(2):585-601. Epub 2005 Sep 8. [Abstract]  
  60. Bammler T, Beyer RP, Bhattacharya S, Boorman GA, Boyles A, Bradford BU, Bumgarner RE, Bushel PR, Chaturvedi K, Choi D, Cunningham ML, Deng S, Dressman HK, Fannin RD, Farin FM, Freedman JH, Fry RC, Harper A, Humble MC, Hurban P, Kavanagh TJ, Kaufmann WK, Kerr KF, Jing L, Lapidus JA, Lasarev MR, Li J, Li YJ,Lobenhofer EK, Lu X, Malek RL, Milton S, Nagalla SR, O'malley JP, Palmer VS, Pattee P, Paules RS, Perou CM, Phillips K, Qin LX, Qiu Y, Quigley SD, Rodland M, Rusyn I, Samson LD, Schwartz DA, Shi Y, Shin JL, Sieber SO, Slifer S, Speer MC, Spencer PS, Sproles DI, Swenberg JA, Suk WA, Sullivan RC, Tian R, Tennant RW, Todd SA, Tucker CJ, Van Houten B, Weis BK, Xuan S, Zarbl H; Members of the Toxicogenomics Research Consortium. Standardizing global gene expression analysis between laboratories and across platforms. Nat Methods. 2005 May;2(5):351-6. [Abstract]  
  61. Chou JW, Paules RS., Bushel PR. Systematic Variation Normalization in Microarray Data to Get Gene Expression Comparison Unbiased. JBCB 3, 225-41, 2005. [Abstract]  
  62. Leung E, Bushel PR. PAGE: phase-shifted analysis of gene expression. Bioinformatics. 2006 Feb 1;22(3):367-8. Epub 2005 Dec 1. [Abstract]  
  63. Bushel PR. Toxicogenomics: Principles and Applications, Chapter5 - Databases for Toxicogenomics. Editors: Hisham K. Hamadeh and Cynthia A. Afshari, John Wiley and Sons, Inc. 2004.  
  64. Mattes WB, Pettit SD, Sansone S, Bushel PR, Waters MD. Database Development in Toxicogenomics: Issues and Efforts. EHP 112(4) 495-505, 2004. [Abstract]  
  65. Qin LX, Kerr KF, Contributing Members of the Toxicogenomics Research Consortium.
    Empirical evaluation of data transformations and ranking statistics for microarray analysis. Nucleic Acids Res. 32(18):5471-5479, 2004. [Abstract]
  66. Xue R., Li, J, Streveler DJ. Microarray Gene Expression Profile Data Mining Model for Clinical Cancer Research Proceeding of the 37th Hawaii International Conference on System Sciences, 2004.  
  67. Bushel PR, Hamadeh HK, Bennett L, Green J, Albeson A, Misener S, Afshari CA, Paules RS. Computational Selection of Distinct Class- and Subclass- Specific Gene Expression Signatures. Journal of Biomedical Informatics 35: 160-170, 2003. [Abstract]  
  68. Heinloth AN, Shackelford RE, Innes CL, Bennett L, Li L, Amin RP, Sieber SO, Flores KG, Bushel PR, Paules RS. Abstract Identification of distinct and common gene expression changes after oxidative stress and gamma and ultraviolet radiation. Molecular Carcinogenesis, 37(2): 65-82, 2003. [Abstract]  
  69. Heinloth AN, Shackelford RE, Innes CL, Bennett L, Li L, Amin RP, Sieber SO, Flores KG, Bushel PR, Paules RS. ATM-dependent and -independent gene expression changes in response to oxidative stress, gamma irradiation, and UV irradiation. Radiation Research 160, 273-290, 2003. [Abstract]  
  70. Kerr MK, Afshari C, Bennett L, Bushel P, Martinez J, Walker N, Churchill GA. Statistical Analysis of a Gene Expression Microarray Experiment. Statistica Sinica 12(1): 203-217, 2002.  
  71. Hamadeh HK, Bushel PR, Jayadev S, Martin K, DiSorbo O, Sieber S, Bennett L, Tennant R, Stoll R, Barrett JC, Blanchard K, Paules RS, Afshari CA. Gene Expression Analysis Reveals Chemical-Specific Profiles. Toxicological Sciences 67: 219-231, 2002. [Abstract]  
  72. Hamadeh HK, Bushel PR, Jayadev J, Martin K, DiSorbo O, Sieber S, Bennett L, Tennant R, Stoll R, Barrett JC, Blanchard K, Paules RS, Afshari CA. Prediction of Compound Signature Using High Density Gene Expression Profiling. Toxicological Sciences, 67: 232-240, 2002. [Abstract]  
  73. Bushel PR, Hamadeh H, Bennett L, Sieber S, Martin K, Nuwaysir EF, Hayes K, Reynolds K, Paules R, Afshari CA.MAPS: A MicroArray Project System for Gene Expression Experiment Information and Data Validation. Bioinformatics 17(6): 564-565, 2001. [Abstract]  
  74. Wolfinger RD, Gibson G, Wolfinger ED, Bennett L, Hamadeh H, Bushel P, Afshari C, Paules RS. Assessing gene significance from cDNA microarray expression data via mixed models. Journal of Computational Biology 8(6): 625-637, 2001. [Abstract]
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