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Variable selection models based on multiple imputation with an application for predicting median effective dose and maximum effect.

Journal of statistical computation and simulation

Wan Y, Datta S, Conklin DJ, Kong M.
PMID: 26412909
J Stat Comput Simul. 2015;85(9):1902-1916. doi: 10.1080/00949655.2014.907801.

The statistical methods for variable selection and prediction could be challenging when missing covariates exist. Although multiple imputation (MI) is a universally accepted technique for solving missing data problem, how to combine the MI results for variable selection is...

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Wan Y, Datta S, Conklin DJ, et al. Variable selection models based on multiple imputation with an application for predicting median effective dose and maximum effect. J Stat Comput Simul. 2015;85(9):1902-1916doi: 10.1080/00949655.2014.907801.
Wan, Y., Datta, S., Conklin, D. J., & Kong, M. (2015). Variable selection models based on multiple imputation with an application for predicting median effective dose and maximum effect. Journal of statistical computation and simulation, 85(9), 1902-1916. https://doi.org/10.1080/00949655.2014.907801
Wan, Y, et al. "Variable selection models based on multiple imputation with an application for predicting median effective dose and maximum effect." Journal of statistical computation and simulation vol. 85,9 (2015): 1902-1916. doi: https://doi.org/10.1080/00949655.2014.907801
Wan Y, Datta S, Conklin DJ, Kong M. Variable selection models based on multiple imputation with an application for predicting median effective dose and maximum effect. J Stat Comput Simul. 2015;85(9):1902-1916. doi: 10.1080/00949655.2014.907801. PMID: 26412909; PMCID: PMC4583148.
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Impact of nutrient excess and endothelial nitric oxide synthase on the plasma metabolite profile in mice.

Frontiers in physiology

Sansbury BE, Bhatnagar A, Hill BG.
PMID: 25505420
Front Physiol. 2014 Nov 25;5:453. doi: 10.3389/fphys.2014.00453. eCollection 2014.

An increase in calorie consumption is associated with the recent rise in obesity prevalence. However, our current understanding of the effects of nutrient excess on major metabolic pathways appears insufficient to develop safe and effective metabolic interventions to prevent...

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Sansbury BE, Bhatnagar A, Hill BG. Impact of nutrient excess and endothelial nitric oxide synthase on the plasma metabolite profile in mice. Front Physiol. 2014;5:453doi: 10.3389/fphys.2014.00453.
Sansbury, B. E., Bhatnagar, A., & Hill, B. G. (2014). Impact of nutrient excess and endothelial nitric oxide synthase on the plasma metabolite profile in mice. Frontiers in physiology, 5453. https://doi.org/10.3389/fphys.2014.00453
Sansbury, Brian E, et al. "Impact of nutrient excess and endothelial nitric oxide synthase on the plasma metabolite profile in mice." Frontiers in physiology vol. 5 (2014): 453. doi: https://doi.org/10.3389/fphys.2014.00453
Sansbury BE, Bhatnagar A, Hill BG. Impact of nutrient excess and endothelial nitric oxide synthase on the plasma metabolite profile in mice. Front Physiol. 2014 Nov 25;5:453. doi: 10.3389/fphys.2014.00453. eCollection 2014. PMID: 25505420; PMCID: PMC4243488.
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FVB/NJ Mice Are a Useful Model for Examining Cardiac Adaptations to Treadmill Exercise.

Frontiers in physiology

Gibb AA, McNally LA, Riggs DW, Conklin DJ, Bhatnagar A, Hill BG.
PMID: 28066267
Front Physiol. 2016 Dec 21;7:636. doi: 10.3389/fphys.2016.00636. eCollection 2016.

Mice are commonly used to examine the mechanisms by which exercise improves cardiometabolic health; however, exercise compliance and adaptations are often strain-dependent or are variable due to inconsistency in exercise training protocols. In this study, we examined nocturnal/diurnal behavior,...

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Gibb AA, McNally LA, Riggs DW, et al. FVB/NJ Mice Are a Useful Model for Examining Cardiac Adaptations to Treadmill Exercise. Front Physiol. 2016;7:636doi: 10.3389/fphys.2016.00636.
Gibb, A. A., McNally, L. A., Riggs, D. W., Conklin, D. J., Bhatnagar, A., & Hill, B. G. (2016). FVB/NJ Mice Are a Useful Model for Examining Cardiac Adaptations to Treadmill Exercise. Frontiers in physiology, 7636. https://doi.org/10.3389/fphys.2016.00636
Gibb, Andrew A, et al. "FVB/NJ Mice Are a Useful Model for Examining Cardiac Adaptations to Treadmill Exercise." Frontiers in physiology vol. 7 (2016): 636. doi: https://doi.org/10.3389/fphys.2016.00636
Gibb AA, McNally LA, Riggs DW, Conklin DJ, Bhatnagar A, Hill BG. FVB/NJ Mice Are a Useful Model for Examining Cardiac Adaptations to Treadmill Exercise. Front Physiol. 2016 Dec 21;7:636. doi: 10.3389/fphys.2016.00636. eCollection 2016. PMID: 28066267; PMCID: PMC5174104.
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A novel method of nicotine quantification in electronic cigarette liquids and aerosols.

Analytical methods : advancing methods and applications

Ogunwale MA, Chen Y, Theis WS, Nantz MH, Conklin DJ, Fu XA.
PMID: 29187865
Anal Methods. 2017 Aug 07;9(29):4261-4266. doi: 10.1039/C7AY00501F. Epub 2017 Jun 19.

The electronic cigarette (e-cigarette) has emerged as popular electronic nicotine delivery devices (ENDs). However, the general safety and validity of e-cigarettes for nicotine delivery efficacy are still not well understood. This study developed a new method for efficient measurement...

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Ogunwale MA, Chen Y, Theis WS, et al. A novel method of nicotine quantification in electronic cigarette liquids and aerosols. Anal Methods. 2017;9(29):4261-4266doi: 10.1039/C7AY00501F.
Ogunwale, M. A., Chen, Y., Theis, W. S., Nantz, M. H., Conklin, D. J., & Fu, X. A. (2017). A novel method of nicotine quantification in electronic cigarette liquids and aerosols. Analytical methods : advancing methods and applications, 9(29), 4261-4266. https://doi.org/10.1039/C7AY00501F
Ogunwale, Mumiye A, et al. "A novel method of nicotine quantification in electronic cigarette liquids and aerosols." Analytical methods : advancing methods and applications vol. 9,29 (2017): 4261-4266. doi: https://doi.org/10.1039/C7AY00501F
Ogunwale MA, Chen Y, Theis WS, Nantz MH, Conklin DJ, Fu XA. A novel method of nicotine quantification in electronic cigarette liquids and aerosols. Anal Methods. 2017 Aug 07;9(29):4261-4266. doi: 10.1039/C7AY00501F. Epub 2017 Jun 19. PMID: 29187865; PMCID: PMC5703602.
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A metabocentric view of cardiac remodeling.

Current opinion in physiology

Hill BG.
PMID: 32818160
Curr Opin Physiol. 2019 Aug;10:43-48. doi: 10.1016/j.cophys.2019.04.004. Epub 2019 Apr 15.

Physiologic and pathologic stressors promote changes in metabolism that are associated with cardiac remodeling. Metabolic alterations in the heart are a summation of responses of several organs and organ systems, which transform the milieu of circulating substrates and stimuli...

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Hill BG. A metabocentric view of cardiac remodeling. Curr Opin Physiol. 2019;10:43-48doi: 10.1016/j.cophys.2019.04.004.
Hill, B. G. (2019). A metabocentric view of cardiac remodeling. Current opinion in physiology, 1043-48. https://doi.org/10.1016/j.cophys.2019.04.004
Hill, Bradford G. "A metabocentric view of cardiac remodeling." Current opinion in physiology vol. 10 (2019): 43-48. doi: https://doi.org/10.1016/j.cophys.2019.04.004
Hill BG. A metabocentric view of cardiac remodeling. Curr Opin Physiol. 2019 Aug;10:43-48. doi: 10.1016/j.cophys.2019.04.004. Epub 2019 Apr 15. PMID: 32818160; PMCID: PMC7430807.
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Statistical Issues and Group Classification in Plasma MicroRNA Studies With Data Application.

Evolutionary bioinformatics online

Rai SN, Qian C, Pan J, McClain M, Eichenberger MR, McClain CJ, Galandiuk S.
PMID: 32313420
Evol Bioinform Online. 2020 Apr 14;16:1176934320913338. doi: 10.1177/1176934320913338. eCollection 2020.

The analysis of plasma microRNAs (miRNAs) has been widely used as a method for finding potential biomarkers for human diseases, especially those with a link to cancer. Methods of analyzing plasma miRNA have been thoroughly discussed from sample extraction...

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Rai SN, Qian C, Pan J, et al. Statistical Issues and Group Classification in Plasma MicroRNA Studies With Data Application. Evol Bioinform Online. 2020;16:1176934320913338doi: 10.1177/1176934320913338.
Rai, S. N., Qian, C., Pan, J., McClain, M., Eichenberger, M. R., McClain, C. J., & Galandiuk, S. (2020). Statistical Issues and Group Classification in Plasma MicroRNA Studies With Data Application. Evolutionary bioinformatics online, 161176934320913338. https://doi.org/10.1177/1176934320913338
Rai, Shesh N, et al. "Statistical Issues and Group Classification in Plasma MicroRNA Studies With Data Application." Evolutionary bioinformatics online vol. 16 (2020): 1176934320913338. doi: https://doi.org/10.1177/1176934320913338
Rai SN, Qian C, Pan J, McClain M, Eichenberger MR, McClain CJ, Galandiuk S. Statistical Issues and Group Classification in Plasma MicroRNA Studies With Data Application. Evol Bioinform Online. 2020 Apr 14;16:1176934320913338. doi: 10.1177/1176934320913338. eCollection 2020. PMID: 32313420; PMCID: PMC7157974.
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Electronic cigarette-generated aldehydes: The contribution of e-liquid components to their formation and the use of urinary aldehyde metabolites as biomarkers of exposure.

Aerosol science and technology : the journal of the American Association for Aerosol Research

Conklin DJ, Ogunwale MA, Chen Y, Theis WS, Nantz MH, Fu XA, Chen LC, Riggs DW, Lorkiewicz P, Bhatnagar A, Srivastava S.
PMID: 31456604
Aerosol Sci Technol. 2018;52(11):1219-1232. doi: 10.1080/02786826.2018.1500013. Epub 2018 Aug 23.

Electronic cigarettes (e-cigarette) have emerged as a popular electronic nicotine delivery system (ENDS) in the last decade. Despite the absence of combustion products and toxins such as carbon monoxide (CO) and tobacco-specific nitrosamines (TSNA), carbonyls including short-chain, toxic aldehydes...

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Conklin DJ, Ogunwale MA, Chen Y, et al. Electronic cigarette-generated aldehydes: The contribution of e-liquid components to their formation and the use of urinary aldehyde metabolites as biomarkers of exposure. Aerosol Sci Technol. 2018;52(11):1219-1232doi: 10.1080/02786826.2018.1500013.
Conklin, D. J., Ogunwale, M. A., Chen, Y., Theis, W. S., Nantz, M. H., Fu, X. A., Chen, L. C., Riggs, D. W., Lorkiewicz, P., Bhatnagar, A., & Srivastava, S. (2018). Electronic cigarette-generated aldehydes: The contribution of e-liquid components to their formation and the use of urinary aldehyde metabolites as biomarkers of exposure. Aerosol science and technology : the journal of the American Association for Aerosol Research, 52(11), 1219-1232. https://doi.org/10.1080/02786826.2018.1500013
Conklin, Daniel J, et al. "Electronic cigarette-generated aldehydes: The contribution of e-liquid components to their formation and the use of urinary aldehyde metabolites as biomarkers of exposure." Aerosol science and technology : the journal of the American Association for Aerosol Research vol. 52,11 (2018): 1219-1232. doi: https://doi.org/10.1080/02786826.2018.1500013
Conklin DJ, Ogunwale MA, Chen Y, Theis WS, Nantz MH, Fu XA, Chen LC, Riggs DW, Lorkiewicz P, Bhatnagar A, Srivastava S. Electronic cigarette-generated aldehydes: The contribution of e-liquid components to their formation and the use of urinary aldehyde metabolites as biomarkers of exposure. Aerosol Sci Technol. 2018;52(11):1219-1232. doi: 10.1080/02786826.2018.1500013. Epub 2018 Aug 23. PMID: 31456604; PMCID: PMC6711607.
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Cardiovascular Effects and Benefits of Exercise.

Frontiers in cardiovascular medicine

Nystoriak MA, Bhatnagar A.
PMID: 30324108
Front Cardiovasc Med. 2018 Sep 28;5:135. doi: 10.3389/fcvm.2018.00135. eCollection 2018.

It is widely accepted that regular physical activity is beneficial for cardiovascular health. Frequent exercise is robustly associated with a decrease in cardiovascular mortality as well as the risk of developing cardiovascular disease. Physically active individuals have lower blood...

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Nystoriak MA, Bhatnagar A. Cardiovascular Effects and Benefits of Exercise. Front Cardiovasc Med. 2018;5:135doi: 10.3389/fcvm.2018.00135.
Nystoriak, M. A., & Bhatnagar, A. (2018). Cardiovascular Effects and Benefits of Exercise. Frontiers in cardiovascular medicine, 5135. https://doi.org/10.3389/fcvm.2018.00135
Nystoriak, Matthew A, and Bhatnagar, Aruni. "Cardiovascular Effects and Benefits of Exercise." Frontiers in cardiovascular medicine vol. 5 (2018): 135. doi: https://doi.org/10.3389/fcvm.2018.00135
Nystoriak MA, Bhatnagar A. Cardiovascular Effects and Benefits of Exercise. Front Cardiovasc Med. 2018 Sep 28;5:135. doi: 10.3389/fcvm.2018.00135. eCollection 2018. PMID: 30324108; PMCID: PMC6172294.
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Atherogenesis and metabolic dysregulation in LDL receptor-knockout rats.

JCI insight

Sithu SD, Malovichko MV, Riggs KA, Wickramasinghe NS, Winner MG, Agarwal A, Hamed-Berair RE, Kalani A, Riggs DW, Bhatnagar A, Srivastava S.
PMID: 28469073
JCI Insight. 2017 May 04;2(9). doi: 10.1172/jci.insight.86442. eCollection 2017 May 04.

Mechanisms of atherogenesis have been studied extensively in genetically engineered mice with disturbed cholesterol metabolism such as those lacking either the LDL receptor (Ldlr) or apolipoprotein E (apoe). Few other animal models of atherosclerosis are available. WT rabbits or...

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Sithu SD, Malovichko MV, Riggs KA, et al. Atherogenesis and metabolic dysregulation in LDL receptor-knockout rats. JCI Insight. 2017;2(9)doi: 10.1172/jci.insight.86442.
Sithu, S. D., Malovichko, M. V., Riggs, K. A., Wickramasinghe, N. S., Winner, M. G., Agarwal, A., Hamed-Berair, R. E., Kalani, A., Riggs, D. W., Bhatnagar, A., & Srivastava, S. (2017). Atherogenesis and metabolic dysregulation in LDL receptor-knockout rats. JCI insight, 2(9), . https://doi.org/10.1172/jci.insight.86442
Sithu, Srinivas D, et al. "Atherogenesis and metabolic dysregulation in LDL receptor-knockout rats." JCI insight vol. 2,9 (2017). doi: https://doi.org/10.1172/jci.insight.86442
Sithu SD, Malovichko MV, Riggs KA, Wickramasinghe NS, Winner MG, Agarwal A, Hamed-Berair RE, Kalani A, Riggs DW, Bhatnagar A, Srivastava S. Atherogenesis and metabolic dysregulation in LDL receptor-knockout rats. JCI Insight. 2017 May 04;2(9). doi: 10.1172/jci.insight.86442. eCollection 2017 May 04. PMID: 28469073; PMCID: PMC5414561.
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Modulation of tumorigenesis by the pro-inflammatory microRNA miR-301a in mouse models of lung cancer and colorectal cancer.

Cell discovery

Ma X, Yan F, Deng Q, Li F, Lu Z, Liu M, Wang L, Conklin DJ, McCracken J, Srivastava S, Bhatnagar A, Li Y.
PMID: 27462406
Cell Discov. 2015 May 19;1:15005. doi: 10.1038/celldisc.2015.5. eCollection 2015.

Lung cancer and colorectal cancer account for over one-third of all cancer deaths in the United States. MicroRNA-301a (miR-301a) is an activator of both nuclear factor-κB (NF-κB) and Stat3, and is overexpressed in both deadly malignancies. In this work,...

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Ma X, Yan F, Deng Q, et al. Modulation of tumorigenesis by the pro-inflammatory microRNA miR-301a in mouse models of lung cancer and colorectal cancer. Cell Discov. 2015;1:15005doi: 10.1038/celldisc.2015.5.
Ma, X., Yan, F., Deng, Q., Li, F., Lu, Z., Liu, M., Wang, L., Conklin, D. J., McCracken, J., Srivastava, S., Bhatnagar, A., & Li, Y. (2015). Modulation of tumorigenesis by the pro-inflammatory microRNA miR-301a in mouse models of lung cancer and colorectal cancer. Cell discovery, 115005. https://doi.org/10.1038/celldisc.2015.5
Ma, Xiaodong, et al. "Modulation of tumorigenesis by the pro-inflammatory microRNA miR-301a in mouse models of lung cancer and colorectal cancer." Cell discovery vol. 1 (2015): 15005. doi: https://doi.org/10.1038/celldisc.2015.5
Ma X, Yan F, Deng Q, Li F, Lu Z, Liu M, Wang L, Conklin DJ, McCracken J, Srivastava S, Bhatnagar A, Li Y. Modulation of tumorigenesis by the pro-inflammatory microRNA miR-301a in mouse models of lung cancer and colorectal cancer. Cell Discov. 2015 May 19;1:15005. doi: 10.1038/celldisc.2015.5. eCollection 2015. PMID: 27462406; PMCID: PMC4860842.
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Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics.

Metabolites

Trainor PJ, DeFilippis AP, Rai SN.
PMID: 28635678
Metabolites. 2017 Jun 21;7(2). doi: 10.3390/metabo7020030.

Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been...

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Trainor PJ, DeFilippis AP, Rai SN. Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics. Metabolites. 2017;7(2)doi: 10.3390/metabo7020030.
Trainor, P. J., DeFilippis, A. P., & Rai, S. N. (2017). Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics. Metabolites, 7(2), . https://doi.org/10.3390/metabo7020030
Trainor, Patrick J, et al. "Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics." Metabolites vol. 7,2 (2017). doi: https://doi.org/10.3390/metabo7020030
Trainor PJ, DeFilippis AP, Rai SN. Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics. Metabolites. 2017 Jun 21;7(2). doi: 10.3390/metabo7020030. PMID: 28635678; PMCID: PMC5488001.
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Aldehyde Detection in Electronic Cigarette Aerosols.

ACS omega

Ogunwale MA, Li M, Ramakrishnam Raju MV, Chen Y, Nantz MH, Conklin DJ, Fu XA.
PMID: 28393137
ACS Omega. 2017 Mar 31;2(3):1207-1214. doi: 10.1021/acsomega.6b00489. Epub 2017 Mar 29.

Acetaldehyde, acrolein, and formaldehyde are the principal toxic aldehydes present in cigarette smoke and contribute to the risk of cardiovascular disease and noncancerous pulmonary disease. The rapid growth of the use of electronic cigarettes (e-cigarettes) has raised concerns over...

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Ogunwale MA, Li M, Ramakrishnam Raju MV, et al. Aldehyde Detection in Electronic Cigarette Aerosols. ACS Omega. 2017;2(3):1207-1214doi: 10.1021/acsomega.6b00489.
Ogunwale, M. A., Li, M., Ramakrishnam Raju, M. V., Chen, Y., Nantz, M. H., Conklin, D. J., & Fu, X. A. (2017). Aldehyde Detection in Electronic Cigarette Aerosols. ACS omega, 2(3), 1207-1214. https://doi.org/10.1021/acsomega.6b00489
Ogunwale, Mumiye A, et al. "Aldehyde Detection in Electronic Cigarette Aerosols." ACS omega vol. 2,3 (2017): 1207-1214. doi: https://doi.org/10.1021/acsomega.6b00489
Ogunwale MA, Li M, Ramakrishnam Raju MV, Chen Y, Nantz MH, Conklin DJ, Fu XA. Aldehyde Detection in Electronic Cigarette Aerosols. ACS Omega. 2017 Mar 31;2(3):1207-1214. doi: 10.1021/acsomega.6b00489. Epub 2017 Mar 29. PMID: 28393137; PMCID: PMC5377270.
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Showing 1 to 12 of 22 entries