Han K, Lee B, Lee D, Heo G, Oh J, Lee S, Apio C, Park T (2024) Forecasting the spread of COVID-19 based on policy, vaccination, and Omicron data. Scientific Reports, 30;14(1):9962
Song H, Han K, Park J, Liu Z, Goo T, Krishnamurthy A, Park T (2024) K-Track-Covid: interactive web-based dashboard for analyzing geographical and temporal spread of COVID-19 in South Korea. Frontiers in Public Health, 12:1347862
Beck NS, Seo Y, Park T, Jun SS, Im JI, Hong SY (2024) Oxidative stress in patients with coronavirus disease and end-stage renal disease: a pilot study. BMC Nephrology, 25(1):155
Cho EJ, Kim B, Yu SJ, Hong SK, Choi Y, Yi NJ, Lee KW, Suh KS, Yoon JH, Park T (2024) Urinary microbiome-based metagenomic signature for the noninvasive diagnosis of hepatocellular carcinoma. British Journal of Cancer, 130(6):970-975
Lee A, Seo J, Park S, Cho Y, Kim G, Li J, Liang L, Park T, Chung W (2024) Type 2 diabetes and its genetic susceptibility are associated with increased severity and mortality of COVID-19 in UK Biobank. Communications Biology, 7(1):122
Moon MK, Ham H, Song SM, Lee C, Goo T, Oh B, Lee S, Kim SW, Park T (2024) The clinical course of hospitalized COVID-19 patients and aggravation risk prediction models: a retrospective, multi-center Korean cohort study. Frontiers in Medicine, 10:1239789
Lee HS, Kim B, Park T (2024) Genome- and epigenome-wide association studies identify susceptibility of CpG sites and regions for metabolic syndrome in a Korean population. Clinical Epigenetics, 16(1):60
Apio C, Han K, Lee D, Lee B, Park T (2024) Development of New Stringency Indices for Nonpharmacological Social Distancing Policies Implemented in Korea During the COVID-19 Pandemic: Random Forest Approach. JMIR Public Health and Surveillance, 10:e47099
Kang E, Ahn T, Park T (2024) Algorithm for Selecting Potential SARS-CoV-2 Dominant Variants based on POS-NT Frequency. Archives of Microbiology & Immunology, doi: 10.26502/ami.936500157
Lee HS, Kim B, Park T (2024) The association between sleep quality and accelerated epigenetic aging with metabolic syndrome in Korean adults. Cilnical Epigenetics, 16(1):92
Lee M, Park T, Shin JY, Park M (2024) A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data. Scientific Reports, 14(1):17851
Shin JE, Shin N, Park T, Park M (2024) Multipartite network analysis to identify environmental and genetic associations of metabolic syndrome in the Korean population. Scientific Reports, 14(1):20283
