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Publications

Kedem B and Pyne S. Estimation of Tail Probabilities By Repeated Augmented Reality. http://www.healthanalytics.net/Publications/Augmented Reality.pdf

Das S, Ghosh P, Sen B, Pyne S, Mukhopadhyay I. Critical Community Size for COVID-19: A Model-Based Approach for Strategic Lockdown Policy. Statistics and Applications. 2020; 18(1): 181-196. https://www.ssca.org.in/media/10_18_1_2020_SA_Indranil_eMmEZhM.pdf

Pyne S, Ray S, Gurewitsch R, Aruru M. Transition from Social Vulnerability to Resiliency vis-à-vis COVID-19. Statistics and Applications. 2020; 18(1). 197-208. https://www.ssca.org.in/media/11_18_1_2_SA_Pyne_IOjLhKt.pdf

Nielsen F, Marti G, Ray S, Pyne S. Clustering Patterns connecting COVID-19 Dynamics and Human Mobility using Optimal Transport. 2020 (In review).

M. Maleki, G. J. McLachlan, R. Gurewitsch, M. Aruru, S. Pyne. A Mixture of Regressions Model of COVID-19 Death Rates and Population Comorbidities. Statistics and Applications. 2020; 18(1). 295-306. https://ssca.org.in/media/18_18_1_2020_SA_SPyne.pdf

Fox GC, von Laszewski G, Wang F, Pyne S. AICov: An Integrative Deep Learning Framework for COVID-19 Forecasting with Population Covariates. 2020. http://healthanalytics.net/Publications/AICoV.pdf

Nielsen F, Marti G, Ray S, and Pyne S. Clustering Patterns connecting COVID-19 Dynamics and Human Mobility using Optimal Transport. Journal of Latex Class Files. 2020; 14(8). http://healthanalytics.net/Publications/OT.pdf

In press: https://www.statisticsviews.com/details/news/11253998/Laymans-abstract-for-paper-on-estimation-of-residential-radon-concentration-in-P.html

Mentzer S.J., et al. Single-cell transcriptional profiling of cells derived from regenerating alveolar ducts. Frontiers in Medicine. 2020. (Accepted for publication).

Ysasi AB, Bennett RD, Wagner W, Valenzuela CD, Servais AB, Tsuda A, Pyne S, et al. Single-cell transcriptional profiling of cells derived from regenerating alveolar ducts. Frontiers in Medicine. 2020; 7:112. doi: https://doi.org/10.3389/fmed.2020.00112

Qi Y, Find Y, Sinclair D, Guo S, Alberich-Jorda M, Lu J, Tenen D, Kharas M, Pyne S. High-Speed Automatic Characterization of Rare Events in Flow Cytometric Data. PLoS ONE. 2020; 15(2): e0228651. doi: https://doi.org/10.1371/journal.pone.0228651.X.

Zhang X, Pyne S, Kedem B. Model selection in Radon Data Fusion. Statistics in Transition New Series. 2020. http://healthanalytics.net/Publications/Radon2020.pdf



Khodayari E. M., Pyne S (Co-senior author), I. Dinu. Association between bivariate expression of key oncogenes and metabolic phenotypes of patients with prostate cancer. Comput Biol Medicine.103:55-63, 2018. PMID: 30340213

Mendu VR, Perugu S, Manne M, Bolla R, Aruru M, Pyne S. Nutrition Atlas of ICMR-National Institute of Nutrition: an informatics platform on nutrition in India Statistics and Applications. 2018;17(1): 209-219

Aruru M, Pyne S. Noncommunicable disease modeling and simulation as a means of understanding childhood obesity and intervention effectiveness. BLDE Univ.J Health Sci. 2018;3(1):3-8

Aruru M, Pyne S. A Big Data Approach to Surveillance of Disease Outbreaks. Visleshana. 2018;2(2):8-10

Pyne S, Lee SX, McLachlan GJ. Nature and Man: The goal of Bio-Security in the Course of Rapid and Inevitable Human Development. J Ind Soc of Agr Stat. 2015; 69(2): 117-125