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Showing 1 to 12 of 23 entries
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Altered Brain Connectivity in Early Postmenopausal Women with Subjective Cognitive Impairment.

Frontiers in neuroscience

Vega JN, Zurkovsky L, Albert K, Melo A, Boyd B, Dumas J, Woodward N, McDonald BC, Saykin AJ, Park JH, Naylor M, Newhouse PA.
PMID: 27721740
Front Neurosci. 2016 Sep 23;10:433. doi: 10.3389/fnins.2016.00433. eCollection 2016.

Cognitive changes after menopause are a common complaint, especially as the loss of estradiol at menopause has been hypothesized to contribute to the higher rates of dementia in women. To explore the neural processes related to subjective cognitive complaints,...

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Vega JN, Zurkovsky L, Albert K, et al. Altered Brain Connectivity in Early Postmenopausal Women with Subjective Cognitive Impairment. Front Neurosci. 2016;10:433doi: 10.3389/fnins.2016.00433.
Vega, J. N., Zurkovsky, L., Albert, K., Melo, A., Boyd, B., Dumas, J., Woodward, N., McDonald, B. C., Saykin, A. J., Park, J. H., Naylor, M., & Newhouse, P. A. (2016). Altered Brain Connectivity in Early Postmenopausal Women with Subjective Cognitive Impairment. Frontiers in neuroscience, 10433. https://doi.org/10.3389/fnins.2016.00433
Vega, Jennifer N, et al. "Altered Brain Connectivity in Early Postmenopausal Women with Subjective Cognitive Impairment." Frontiers in neuroscience vol. 10 (2016): 433. doi: https://doi.org/10.3389/fnins.2016.00433
Vega JN, Zurkovsky L, Albert K, Melo A, Boyd B, Dumas J, Woodward N, McDonald BC, Saykin AJ, Park JH, Naylor M, Newhouse PA. Altered Brain Connectivity in Early Postmenopausal Women with Subjective Cognitive Impairment. Front Neurosci. 2016 Sep 23;10:433. doi: 10.3389/fnins.2016.00433. eCollection 2016. PMID: 27721740; PMCID: PMC5034407.
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Methods for optimizing statistical analyses in pharmacogenomics research.

Expert review of clinical pharmacology

Turner SD, Crawford DC, Ritchie MD.
PMID: 20221410
Expert Rev Clin Pharmacol. 2009 Sep 01;2(5):559-570. doi: 10.1586/ecp.09.32.

Pharmacogenomics is a rapidly developing sector of human genetics research with arguably the highest potential for immediate benefit. There is a considerable body of evidence demonstrating that variability in drug-treatment response can be explained in part by genetic variation....

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Turner SD, Crawford DC, Ritchie MD. Methods for optimizing statistical analyses in pharmacogenomics research. Expert Rev Clin Pharmacol. 2009;2(5):559-570doi: 10.1586/ecp.09.32.
Turner, S. D., Crawford, D. C., & Ritchie, M. D. (2009). Methods for optimizing statistical analyses in pharmacogenomics research. Expert review of clinical pharmacology, 2(5), 559-570. https://doi.org/10.1586/ecp.09.32
Turner, Stephen D, et al. "Methods for optimizing statistical analyses in pharmacogenomics research." Expert review of clinical pharmacology vol. 2,5 (2009): 559-570. doi: https://doi.org/10.1586/ecp.09.32
Turner SD, Crawford DC, Ritchie MD. Methods for optimizing statistical analyses in pharmacogenomics research. Expert Rev Clin Pharmacol. 2009 Sep 01;2(5):559-570. doi: 10.1586/ecp.09.32. PMID: 20221410; PMCID: PMC2835152.
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ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci.

BioData mining

Turner SD, Dudek SM, Ritchie MD.
PMID: 20875103
BioData Min. 2010 Sep 27;3(1):5. doi: 10.1186/1756-0381-3-5.

BACKGROUND: Growing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex traits has been explained by...

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Turner SD, Dudek SM, Ritchie MD. ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci. BioData Min. 2010;3(1):5doi: 10.1186/1756-0381-3-5.
Turner, S. D., Dudek, S. M., & Ritchie, M. D. (2010). ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci. BioData mining, 3(1), 5. https://doi.org/10.1186/1756-0381-3-5
Turner, Stephen D, et al. "ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci." BioData mining vol. 3,1 (2010): 5. doi: https://doi.org/10.1186/1756-0381-3-5
Turner SD, Dudek SM, Ritchie MD. ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci. BioData Min. 2010 Sep 27;3(1):5. doi: 10.1186/1756-0381-3-5. PMID: 20875103; PMCID: PMC2955681.
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The multiscale backbone of the human phenotype network based on biological pathways.

BioData mining

Darabos C, White MJ, Graham BE, Leung DN, Williams SM, Moore JH.
PMID: 24460644
BioData Min. 2014 Jan 25;7(1):1. doi: 10.1186/1756-0381-7-1.

BACKGROUND: Networks are commonly used to represent and analyze large and complex systems of interacting elements. In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built pathway-based human phenotype network (PHPN) of over...

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Darabos C, White MJ, Graham BE, et al. The multiscale backbone of the human phenotype network based on biological pathways. BioData Min. 2014;7(1):1doi: 10.1186/1756-0381-7-1.
Darabos, C., White, M. J., Graham, B. E., Leung, D. N., Williams, S. M., & Moore, J. H. (2014). The multiscale backbone of the human phenotype network based on biological pathways. BioData mining, 7(1), 1. https://doi.org/10.1186/1756-0381-7-1
Darabos, Christian, et al. "The multiscale backbone of the human phenotype network based on biological pathways." BioData mining vol. 7,1 (2014): 1. doi: https://doi.org/10.1186/1756-0381-7-1
Darabos C, White MJ, Graham BE, Leung DN, Williams SM, Moore JH. The multiscale backbone of the human phenotype network based on biological pathways. BioData Min. 2014 Jan 25;7(1):1. doi: 10.1186/1756-0381-7-1. PMID: 24460644; PMCID: PMC3924922.
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Phenotyping Adverse Drug Reactions: Statin-Related Myotoxicity.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science

Wiley LK, Moretz JD, Denny JC, Peterson JF, Bush WS.
PMID: 26306287
AMIA Jt Summits Transl Sci Proc. 2015 Mar 25;2015:466-70. eCollection 2015.

It is unclear the extent to which best practices for phenotyping disease states from electronic medical records (EMRs) translate to phenotyping adverse drug events. Here we use statin-induced myotoxicity as a case study to identify best practices in this...

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Wiley LK, Moretz JD, Denny JC, et al. Phenotyping Adverse Drug Reactions: Statin-Related Myotoxicity. AMIA Jt Summits Transl Sci Proc. 2015;2015:466-70
Wiley, L. K., Moretz, J. D., Denny, J. C., Peterson, J. F., & Bush, W. S. (2015). Phenotyping Adverse Drug Reactions: Statin-Related Myotoxicity. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2015466-70.
Wiley, Laura K, et al. "Phenotyping Adverse Drug Reactions: Statin-Related Myotoxicity." AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science vol. 2015 (2015): 466-70.
Wiley LK, Moretz JD, Denny JC, Peterson JF, Bush WS. Phenotyping Adverse Drug Reactions: Statin-Related Myotoxicity. AMIA Jt Summits Transl Sci Proc. 2015 Mar 25;2015:466-70. eCollection 2015. PMID: 26306287; PMCID: PMC4525276.
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Fate or coincidence: do COPD and major depression share genetic risk factors?.

Human molecular genetics

Martucci VL, Richmond B, Davis LK, Blackwell TS, Cox NJ, Samuels D, Velez Edwards D, Aldrich MC.
PMID: 33704461
Hum Mol Genet. 2021 May 12;30(7):619-628. doi: 10.1093/hmg/ddab068.

Major depressive disorder (MDD) is a common comorbidity in chronic obstructive pulmonary disease (COPD), affecting up to 57% of patients with COPD. Although the comorbidity of COPD and MDD is well established, the causal relationship between these two diseases...

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Martucci VL, Richmond B, Davis LK, et al. Fate or coincidence: do COPD and major depression share genetic risk factors?. Hum Mol Genet. 2021;30(7):619-628doi: 10.1093/hmg/ddab068.
Martucci, V. L., Richmond, B., Davis, L. K., Blackwell, T. S., Cox, N. J., Samuels, D., Velez Edwards, D., & Aldrich, M. C. (2021). Fate or coincidence: do COPD and major depression share genetic risk factors?. Human molecular genetics, 30(7), 619-628. https://doi.org/10.1093/hmg/ddab068
Martucci, Victoria L, et al. "Fate or coincidence: do COPD and major depression share genetic risk factors?." Human molecular genetics vol. 30,7 (2021): 619-628. doi: https://doi.org/10.1093/hmg/ddab068
Martucci VL, Richmond B, Davis LK, Blackwell TS, Cox NJ, Samuels D, Velez Edwards D, Aldrich MC. Fate or coincidence: do COPD and major depression share genetic risk factors?. Hum Mol Genet. 2021 May 12;30(7):619-628. doi: 10.1093/hmg/ddab068. PMID: 33704461; PMCID: PMC8120137.
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Mitochondrial Haplogroups as a Risk Factor for Herpes Zoster.

Open forum infectious diseases

Levinson RT, Hulgan T, Kalams SA, Fessel JP, Samuels DC.
PMID: 27807590
Open Forum Infect Dis. 2016 Oct 19;3(4):ofw184. doi: 10.1093/ofid/ofw184. eCollection 2016 Oct.

No abstract available.

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Levinson RT, Hulgan T, Kalams SA, et al. Mitochondrial Haplogroups as a Risk Factor for Herpes Zoster. Open Forum Infect Dis. 2016;3(4):ofw184doi: 10.1093/ofid/ofw184.
Levinson, R. T., Hulgan, T., Kalams, S. A., Fessel, J. P., & Samuels, D. C. (2016). Mitochondrial Haplogroups as a Risk Factor for Herpes Zoster. Open forum infectious diseases, 3(4), ofw184. https://doi.org/10.1093/ofid/ofw184
Levinson, Rebecca T, et al. "Mitochondrial Haplogroups as a Risk Factor for Herpes Zoster." Open forum infectious diseases vol. 3,4 (2016): ofw184. doi: https://doi.org/10.1093/ofid/ofw184
Levinson RT, Hulgan T, Kalams SA, Fessel JP, Samuels DC. Mitochondrial Haplogroups as a Risk Factor for Herpes Zoster. Open Forum Infect Dis. 2016 Oct 19;3(4):ofw184. doi: 10.1093/ofid/ofw184. eCollection 2016 Oct. PMID: 27807590; PMCID: PMC5088697.
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Tracing the Evolution of Human Gene Regulation and Its Association with Shifts in Environment.

Genome biology and evolution

Colbran LL, Johnson MR, Mathieson I, Capra JA.
PMID: 34718543
Genome Biol Evol. 2021 Nov 05;13(11). doi: 10.1093/gbe/evab237.

As humans populated the world, they adapted to many varying environmental factors, including climate, diet, and pathogens. Because many of these adaptations were mediated by multiple noncoding variants with small effects on gene regulation, it has been difficult to...

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Colbran LL, Johnson MR, Mathieson I, et al. Tracing the Evolution of Human Gene Regulation and Its Association with Shifts in Environment. Genome Biol Evol. 2021;13(11)doi: 10.1093/gbe/evab237.
Colbran, L. L., Johnson, M. R., Mathieson, I., & Capra, J. A. (2021). Tracing the Evolution of Human Gene Regulation and Its Association with Shifts in Environment. Genome biology and evolution, 13(11), . https://doi.org/10.1093/gbe/evab237
Colbran, Laura L, et al. "Tracing the Evolution of Human Gene Regulation and Its Association with Shifts in Environment." Genome biology and evolution vol. 13,11 (2021). doi: https://doi.org/10.1093/gbe/evab237
Colbran LL, Johnson MR, Mathieson I, Capra JA. Tracing the Evolution of Human Gene Regulation and Its Association with Shifts in Environment. Genome Biol Evol. 2021 Nov 05;13(11). doi: 10.1093/gbe/evab237. PMID: 34718543; PMCID: PMC8576593.
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Modeling the Evolutionary Architectures of Transcribed Human Enhancer Sequences Reveals Distinct Origins, Functions, and Associations with Human Trait Variation.

Molecular biology and evolution

Fong SL, Capra JA.
PMID: 33973014
Mol Biol Evol. 2021 Aug 23;38(9):3681-3696. doi: 10.1093/molbev/msab138.

Despite the importance of gene regulatory enhancers in human biology and evolution, we lack a comprehensive model of enhancer evolution and function. This substantially limits our understanding of the genetic basis of species divergence and our ability to interpret...

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Fong SL, Capra JA. Modeling the Evolutionary Architectures of Transcribed Human Enhancer Sequences Reveals Distinct Origins, Functions, and Associations with Human Trait Variation. Mol Biol Evol. 2021;38(9):3681-3696doi: 10.1093/molbev/msab138.
Fong, S. L., & Capra, J. A. (2021). Modeling the Evolutionary Architectures of Transcribed Human Enhancer Sequences Reveals Distinct Origins, Functions, and Associations with Human Trait Variation. Molecular biology and evolution, 38(9), 3681-3696. https://doi.org/10.1093/molbev/msab138
Fong, Sarah L, and Capra, John A. "Modeling the Evolutionary Architectures of Transcribed Human Enhancer Sequences Reveals Distinct Origins, Functions, and Associations with Human Trait Variation." Molecular biology and evolution vol. 38,9 (2021): 3681-3696. doi: https://doi.org/10.1093/molbev/msab138
Fong SL, Capra JA. Modeling the Evolutionary Architectures of Transcribed Human Enhancer Sequences Reveals Distinct Origins, Functions, and Associations with Human Trait Variation. Mol Biol Evol. 2021 Aug 23;38(9):3681-3696. doi: 10.1093/molbev/msab138. PMID: 33973014; PMCID: PMC8382917.
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The effects of linkage disequilibrium in large scale SNP datasets for MDR.

BioData mining

Grady BJ, Torstenson ES, Ritchie MD.
PMID: 21545716
BioData Min. 2011 May 05;4:11. doi: 10.1186/1756-0381-4-11.

BACKGROUND: In the analysis of large-scale genomic datasets, an important consideration is the power of analytical methods to identify accurate predictive models of disease. When trying to assess sensitivity from such analytical methods, a confounding factor up to this...

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Grady BJ, Torstenson ES, Ritchie MD. The effects of linkage disequilibrium in large scale SNP datasets for MDR. BioData Min. 2011;4:11doi: 10.1186/1756-0381-4-11.
Grady, B. J., Torstenson, E. S., & Ritchie, M. D. (2011). The effects of linkage disequilibrium in large scale SNP datasets for MDR. BioData mining, 411. https://doi.org/10.1186/1756-0381-4-11
Grady, Benjamin J, et al. "The effects of linkage disequilibrium in large scale SNP datasets for MDR." BioData mining vol. 4 (2011): 11. doi: https://doi.org/10.1186/1756-0381-4-11
Grady BJ, Torstenson ES, Ritchie MD. The effects of linkage disequilibrium in large scale SNP datasets for MDR. BioData Min. 2011 May 05;4:11. doi: 10.1186/1756-0381-4-11. PMID: 21545716; PMCID: PMC3108918.
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Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors.

BioData mining

Ciesielski TH, Pendergrass SA, White MJ, Kodaman N, Sobota RS, Huang M, Bartlett J, Li J, Pan Q, Gui J, Selleck SB, Amos CI, Ritchie MD, Moore JH, Williams SM.
PMID: 25071867
BioData Min. 2014 Jun 30;7:10. doi: 10.1186/1756-0381-7-10. eCollection 2014.

In omic research, such as genome wide association studies, researchers seek to repeat their results in other datasets to reduce false positive findings and thus provide evidence for the existence of true associations. Unfortunately this standard validation approach cannot...

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Ciesielski TH, Pendergrass SA, White MJ, et al. Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors. BioData Min. 2014;7:10doi: 10.1186/1756-0381-7-10.
Ciesielski, T. H., Pendergrass, S. A., White, M. J., Kodaman, N., Sobota, R. S., Huang, M., Bartlett, J., Li, J., Pan, Q., Gui, J., Selleck, S. B., Amos, C. I., Ritchie, M. D., Moore, J. H., & Williams, S. M. (2014). Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors. BioData mining, 710. https://doi.org/10.1186/1756-0381-7-10
Ciesielski, Timothy H, et al. "Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors." BioData mining vol. 7 (2014): 10. doi: https://doi.org/10.1186/1756-0381-7-10
Ciesielski TH, Pendergrass SA, White MJ, Kodaman N, Sobota RS, Huang M, Bartlett J, Li J, Pan Q, Gui J, Selleck SB, Amos CI, Ritchie MD, Moore JH, Williams SM. Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors. BioData Min. 2014 Jun 30;7:10. doi: 10.1186/1756-0381-7-10. eCollection 2014. PMID: 25071867; PMCID: PMC4112852.
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Correction: Phylosymbiosis: Relationships and Functional Effects of Microbial Communities across Host Evolutionary History.

PLoS biology

Brooks AW, Kohl KD, Brucker RM, van Opstal EJ, Bordenstein SR.
PMID: 28068336
PLoS Biol. 2017 Jan 09;15(1):e1002587. doi: 10.1371/journal.pbio.1002587. eCollection 2017 Jan.

[This corrects the article DOI: 10.1371/journal.pbio.2000225.].

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Brooks AW, Kohl KD, Brucker RM, et al. Correction: Phylosymbiosis: Relationships and Functional Effects of Microbial Communities across Host Evolutionary History. PLoS Biol. 2017;15(1):e1002587doi: 10.1371/journal.pbio.1002587.
Brooks, A. W., Kohl, K. D., Brucker, R. M., van Opstal, E. J., & Bordenstein, S. R. (2017). Correction: Phylosymbiosis: Relationships and Functional Effects of Microbial Communities across Host Evolutionary History. PLoS biology, 15(1), e1002587. https://doi.org/10.1371/journal.pbio.1002587
Brooks, Andrew W, et al. "Correction: Phylosymbiosis: Relationships and Functional Effects of Microbial Communities across Host Evolutionary History." PLoS biology vol. 15,1 (2017): e1002587. doi: https://doi.org/10.1371/journal.pbio.1002587
Brooks AW, Kohl KD, Brucker RM, van Opstal EJ, Bordenstein SR. Correction: Phylosymbiosis: Relationships and Functional Effects of Microbial Communities across Host Evolutionary History. PLoS Biol. 2017 Jan 09;15(1):e1002587. doi: 10.1371/journal.pbio.1002587. eCollection 2017 Jan. PMID: 28068336; PMCID: PMC5222595.
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Showing 1 to 12 of 23 entries