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Revisiting inconsistency in large pharmacogenomic studies.

F1000Research

Safikhani Z, Smirnov P, Freeman M, El-Hachem N, She A, Rene Q, Goldenberg A, Birkbak NJ, Hatzis C, Shi L, Beck AH, Aerts HJWL, Quackenbush J, Haibe-Kains B.
PMID: 28928933
F1000Res. 2016 Sep 16;5:2333. doi: 10.12688/f1000research.9611.3. eCollection 2016.

In 2013, we published a comparative analysis of mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer...

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Safikhani Z, Smirnov P, Freeman M, et al. Revisiting inconsistency in large pharmacogenomic studies. F1000Res. 2016;5:2333doi: 10.12688/f1000research.9611.3.
Safikhani, Z., Smirnov, P., Freeman, M., El-Hachem, N., She, A., Rene, Q., Goldenberg, A., Birkbak, N. J., Hatzis, C., Shi, L., Beck, A. H., Aerts, H. J. W. L., Quackenbush, J., & Haibe-Kains, B. (2016). Revisiting inconsistency in large pharmacogenomic studies. F1000Research, 52333. https://doi.org/10.12688/f1000research.9611.3
Safikhani, Zhaleh, et al. "Revisiting inconsistency in large pharmacogenomic studies." F1000Research vol. 5 (2016): 2333. doi: https://doi.org/10.12688/f1000research.9611.3
Safikhani Z, Smirnov P, Freeman M, El-Hachem N, She A, Rene Q, Goldenberg A, Birkbak NJ, Hatzis C, Shi L, Beck AH, Aerts HJWL, Quackenbush J, Haibe-Kains B. Revisiting inconsistency in large pharmacogenomic studies. F1000Res. 2016 Sep 16;5:2333. doi: 10.12688/f1000research.9611.3. eCollection 2016. PMID: 28928933; PMCID: PMC5580432.
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Assessment of pharmacogenomic agreement.

F1000Research

Safikhani Z, El-Hachem N, Quevedo R, Smirnov P, Goldenberg A, Juul Birkbak N, Mason C, Hatzis C, Shi L, Aerts HJ, Quackenbush J, Haibe-Kains B.
PMID: 27408686
F1000Res. 2016 May 09;5:825. doi: 10.12688/f1000research.8705.1. eCollection 2016.

In 2013 we published an analysis demonstrating that drug response data and gene-drug associations reported in two independent large-scale pharmacogenomic screens, Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE), were inconsistent. The GDSC and...

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Safikhani Z, El-Hachem N, Quevedo R, et al. Assessment of pharmacogenomic agreement. F1000Res. 2016;5:825doi: 10.12688/f1000research.8705.1.
Safikhani, Z., El-Hachem, N., Quevedo, R., Smirnov, P., Goldenberg, A., Juul Birkbak, N., Mason, C., Hatzis, C., Shi, L., Aerts, H. J., Quackenbush, J., & Haibe-Kains, B. (2016). Assessment of pharmacogenomic agreement. F1000Research, 5825. https://doi.org/10.12688/f1000research.8705.1
Safikhani, Zhaleh, et al. "Assessment of pharmacogenomic agreement." F1000Research vol. 5 (2016): 825. doi: https://doi.org/10.12688/f1000research.8705.1
Safikhani Z, El-Hachem N, Quevedo R, Smirnov P, Goldenberg A, Juul Birkbak N, Mason C, Hatzis C, Shi L, Aerts HJ, Quackenbush J, Haibe-Kains B. Assessment of pharmacogenomic agreement. F1000Res. 2016 May 09;5:825. doi: 10.12688/f1000research.8705.1. eCollection 2016. PMID: 27408686; PMCID: PMC4926729.
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Estimating gene regulatory networks with pandaR.

Bioinformatics (Oxford, England)

Schlauch D, Paulson JN, Young A, Glass K, Quackenbush J.
PMID: 28334344
Bioinformatics. 2017 Jul 15;33(14):2232-2234. doi: 10.1093/bioinformatics/btx139.

CONTACT: [email protected] or [email protected] AND IMPLEMENTATION: PandaR is provided as a Bioconductor R Package and is available at bioconductor.org/packages/pandaR.

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Schlauch D, Paulson JN, Young A, et al. Estimating gene regulatory networks with pandaR. Bioinformatics. 2017;33(14):2232-2234doi: 10.1093/bioinformatics/btx139.
Schlauch, D., Paulson, J. N., Young, A., Glass, K., & Quackenbush, J. (2017). Estimating gene regulatory networks with pandaR. Bioinformatics (Oxford, England), 33(14), 2232-2234. https://doi.org/10.1093/bioinformatics/btx139
Schlauch, Daniel, et al. "Estimating gene regulatory networks with pandaR." Bioinformatics (Oxford, England) vol. 33,14 (2017): 2232-2234. doi: https://doi.org/10.1093/bioinformatics/btx139
Schlauch D, Paulson JN, Young A, Glass K, Quackenbush J. Estimating gene regulatory networks with pandaR. Bioinformatics. 2017 Jul 15;33(14):2232-2234. doi: 10.1093/bioinformatics/btx139. PMID: 28334344; PMCID: PMC5870629.
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Detecting phenotype-driven transitions in regulatory network structure.

NPJ systems biology and applications

Padi M, Quackenbush J.
PMID: 29707235
NPJ Syst Biol Appl. 2018 Apr 19;4:16. doi: 10.1038/s41540-018-0052-5. eCollection 2018.

Complex traits and diseases like human height or cancer are often not caused by a single mutation or genetic variant, but instead arise from functional changes in the underlying molecular network. Biological networks are known to be highly modular...

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Padi M, Quackenbush J. Detecting phenotype-driven transitions in regulatory network structure. NPJ Syst Biol Appl. 2018;4:16doi: 10.1038/s41540-018-0052-5.
Padi, M., & Quackenbush, J. (2018). Detecting phenotype-driven transitions in regulatory network structure. NPJ systems biology and applications, 416. https://doi.org/10.1038/s41540-018-0052-5
Padi, Megha, and Quackenbush, John. "Detecting phenotype-driven transitions in regulatory network structure." NPJ systems biology and applications vol. 4 (2018): 16. doi: https://doi.org/10.1038/s41540-018-0052-5
Padi M, Quackenbush J. Detecting phenotype-driven transitions in regulatory network structure. NPJ Syst Biol Appl. 2018 Apr 19;4:16. doi: 10.1038/s41540-018-0052-5. eCollection 2018. PMID: 29707235; PMCID: PMC5908977.
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Histopathological Image QTL Discovery of Immune Infiltration Variants.

iScience

Barry JD, Fagny M, Paulson JN, Aerts HJWL, Platig J, Quackenbush J.
PMID: 30240647
iScience. 2018 Jul 27;5:80-89. doi: 10.1016/j.isci.2018.07.001. Epub 2018 Jul 06.

Genotype-to-phenotype association studies typically use macroscopic physiological measurements or molecular readouts as quantitative traits. There are comparatively few suitable quantitative traits available between cell and tissue length scales, a limitation that hinders our ability to identify variants affecting phenotype...

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Barry JD, Fagny M, Paulson JN, et al. Histopathological Image QTL Discovery of Immune Infiltration Variants. iScience. 2018;5:80-89doi: 10.1016/j.isci.2018.07.001.
Barry, J. D., Fagny, M., Paulson, J. N., Aerts, H. J. W. L., Platig, J., & Quackenbush, J. (2018). Histopathological Image QTL Discovery of Immune Infiltration Variants. iScience, 580-89. https://doi.org/10.1016/j.isci.2018.07.001
Barry, Joseph D, et al. "Histopathological Image QTL Discovery of Immune Infiltration Variants." iScience vol. 5 (2018): 80-89. doi: https://doi.org/10.1016/j.isci.2018.07.001
Barry JD, Fagny M, Paulson JN, Aerts HJWL, Platig J, Quackenbush J. Histopathological Image QTL Discovery of Immune Infiltration Variants. iScience. 2018 Jul 27;5:80-89. doi: 10.1016/j.isci.2018.07.001. Epub 2018 Jul 06. PMID: 30240647; PMCID: PMC6123851.
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Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes.

NPJ systems biology and applications

Sharma A, Halu A, Decano JL, Padi M, Liu YY, Prasad RB, Fadista J, Santolini M, Menche J, Weiss ST, Vidal M, Silverman EK, Aikawa M, Barabási AL, Groop L, Loscalzo J.
PMID: 29977601
NPJ Syst Biol Appl. 2018 Jul 03;4:25. doi: 10.1038/s41540-018-0057-0. eCollection 2018.

Probing the dynamic control features of biological networks represents a new frontier in capturing the dysregulated pathways in complex diseases. Here, using patient samples obtained from a pancreatic islet transplantation program, we constructed a tissue-specific gene regulatory network and...

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Sharma A, Halu A, Decano JL, et al. Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes. NPJ Syst Biol Appl. 2018;4:25doi: 10.1038/s41540-018-0057-0.
Sharma, A., Halu, A., Decano, J. L., Padi, M., Liu, Y. Y., Prasad, R. B., Fadista, J., Santolini, M., Menche, J., Weiss, S. T., Vidal, M., Silverman, E. K., Aikawa, M., Barabási, A. L., Groop, L., & Loscalzo, J. (2018). Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes. NPJ systems biology and applications, 425. https://doi.org/10.1038/s41540-018-0057-0
Sharma, Amitabh, et al. "Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes." NPJ systems biology and applications vol. 4 (2018): 25. doi: https://doi.org/10.1038/s41540-018-0057-0
Sharma A, Halu A, Decano JL, Padi M, Liu YY, Prasad RB, Fadista J, Santolini M, Menche J, Weiss ST, Vidal M, Silverman EK, Aikawa M, Barabási AL, Groop L, Loscalzo J. Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes. NPJ Syst Biol Appl. 2018 Jul 03;4:25. doi: 10.1038/s41540-018-0057-0. eCollection 2018. PMID: 29977601; PMCID: PMC6028434.
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Haploinsufficiency of Hedgehog interacting protein causes increased emphysema induced by cigarette smoke through network rewiring.

Genome medicine

Lao T, Glass K, Qiu W, Polverino F, Gupta K, Morrow J, Mancini JD, Vuong L, Perrella MA, Hersh CP, Owen CA, Quackenbush J, Yuan GC, Silverman EK, Zhou X.
PMID: 25763110
Genome Med. 2015 Feb 14;7(1):12. doi: 10.1186/s13073-015-0137-3. eCollection 2015.

BACKGROUND: The HHIP gene, encoding Hedgehog interacting protein, has been implicated in chronic obstructive pulmonary disease (COPD) by genome-wide association studies (GWAS), and our subsequent studies identified a functional upstream genetic variant that decreased HHIP transcription. However, little is...

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Lao T, Glass K, Qiu W, et al. Haploinsufficiency of Hedgehog interacting protein causes increased emphysema induced by cigarette smoke through network rewiring. Genome Med. 2015;7(1):12doi: 10.1186/s13073-015-0137-3.
Lao, T., Glass, K., Qiu, W., Polverino, F., Gupta, K., Morrow, J., Mancini, J. D., Vuong, L., Perrella, M. A., Hersh, C. P., Owen, C. A., Quackenbush, J., Yuan, G. C., Silverman, E. K., & Zhou, X. (2015). Haploinsufficiency of Hedgehog interacting protein causes increased emphysema induced by cigarette smoke through network rewiring. Genome medicine, 7(1), 12. https://doi.org/10.1186/s13073-015-0137-3
Lao, Taotao, et al. "Haploinsufficiency of Hedgehog interacting protein causes increased emphysema induced by cigarette smoke through network rewiring." Genome medicine vol. 7,1 (2015): 12. doi: https://doi.org/10.1186/s13073-015-0137-3
Lao T, Glass K, Qiu W, Polverino F, Gupta K, Morrow J, Mancini JD, Vuong L, Perrella MA, Hersh CP, Owen CA, Quackenbush J, Yuan GC, Silverman EK, Zhou X. Haploinsufficiency of Hedgehog interacting protein causes increased emphysema induced by cigarette smoke through network rewiring. Genome Med. 2015 Feb 14;7(1):12. doi: 10.1186/s13073-015-0137-3. eCollection 2015. PMID: 25763110; PMCID: PMC4355149.
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DNA methylation profiling in human lung tissue identifies genes associated with COPD.

Epigenetics

Morrow JD, Cho MH, Hersh CP, Pinto-Plata V, Celli B, Marchetti N, Criner G, Bueno R, Washko G, Glass K, Choi AMK, Quackenbush J, Silverman EK, DeMeo DL.
PMID: 27564456
Epigenetics. 2016 Oct 02;11(10):730-739. doi: 10.1080/15592294.2016.1226451. Epub 2016 Nov 01.

Chronic obstructive pulmonary disease (COPD) is a smoking-related disease characterized by genetic and phenotypic heterogeneity. Although association studies have identified multiple genomic regions with replicated associations to COPD, genetic variation only partially explains the susceptibility to lung disease, and...

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Morrow JD, Cho MH, Hersh CP, et al. DNA methylation profiling in human lung tissue identifies genes associated with COPD. Epigenetics. 2016;11(10):730-739doi: 10.1080/15592294.2016.1226451.
Morrow, J. D., Cho, M. H., Hersh, C. P., Pinto-Plata, V., Celli, B., Marchetti, N., Criner, G., Bueno, R., Washko, G., Glass, K., Choi, A. M. K., Quackenbush, J., Silverman, E. K., & DeMeo, D. L. (2016). DNA methylation profiling in human lung tissue identifies genes associated with COPD. Epigenetics, 11(10), 730-739. https://doi.org/10.1080/15592294.2016.1226451
Morrow, Jarrett D, et al. "DNA methylation profiling in human lung tissue identifies genes associated with COPD." Epigenetics vol. 11,10 (2016): 730-739. doi: https://doi.org/10.1080/15592294.2016.1226451
Morrow JD, Cho MH, Hersh CP, Pinto-Plata V, Celli B, Marchetti N, Criner G, Bueno R, Washko G, Glass K, Choi AMK, Quackenbush J, Silverman EK, DeMeo DL. DNA methylation profiling in human lung tissue identifies genes associated with COPD. Epigenetics. 2016 Oct 02;11(10):730-739. doi: 10.1080/15592294.2016.1226451. Epub 2016 Nov 01. PMID: 27564456; PMCID: PMC5094634.
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Learning to share.

Scientific American

Quackenbush J.
PMID: 24974706
Sci Am. 2014 Jul;311(1):S22.

No abstract available.

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Quackenbush J. Learning to share. Sci Am. 2014;311(1):S22
Quackenbush, J. (2014). Learning to share. Scientific American, 311(1), S22.
Quackenbush, John. "Learning to share." Scientific American vol. 311,1 (2014): S22.
Quackenbush J. Learning to share. Sci Am. 2014 Jul;311(1):S22. PMID: 24974706; PMCID: PMC4446053.
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Showing 1 to 9 of 9 entries