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Seong-Eui Hong

Showing results (11-20 of 22) with videos related to

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Biotechnology Journal|March 6, 2008
Identification of mouse heart transcriptomic network sensitive to various heart diseasesSeong-Eui Hong, Inju Park, Hyeseon Cha, et al.
The International Journal of Biochemistry & Cell Biology|June 17, 2014
Sumoylation regulates ER stress response by modulating calreticulin gene expression in XBP-1-dependent mode in Caenorhabditis elegansYunki Lim, Dukgyu Lee, Karunambigai Kalichamy, et al.
Plos One|March 14, 2015
miR-185 plays an anti-hypertrophic role in the heart via multiple targets in the calcium-signaling pathwaysJin Ock Kim, Dong Woo Song, Eun Jeong Kwon, et al.
Journal of Molecular and Cellular Cardiology|June 30, 2007
Comprehensive identification and characterization of novel cardiac genes in mouseInju Park, Seong-Eui Hong, Tae-Wan Kim, et al.
Scientific Reports|January 31, 2024
DEEPOMICS FFPE, a deep neural network model, identifies DNA sequencing artifacts from formalin fixed paraffin embedded tissue with high accuracyDong-Hyuk Heo, Inyoung Kim, Heejae Seo, et al.
Oncology Reports|November 1, 2013
SC35 promotes splicing of the C5-V6-C6 isoform of CD44 pre-mRNATiing Jen Loh, Heegyum Moon, Sunghee Cho, et al.
Age (Dordrecht, Netherlands)|July 26, 2012
Identification of the dichotomous role of age-related LCK in calorie restriction revealed by integrative analysis of cDNA microarray and interactomeDaeui Park, Eun Kyeong Lee, Eun Jee Jang, et al.
Physiological Genomics|March 1, 2007
Gene profiling during regression of pressure overload-induced cardiac hypertrophyDong Kwon Yang, Bo Youn Choi, Young-Hoon Lee, et al.
Age (Dordrecht, Netherlands)|July 11, 2009
Revealing system-level correlations between aging and calorie restriction using a mouse transcriptomeSeong-Eui Hong, Hyoung-Sam Heo, Dae Hyun Kim, et al.
Frontiers in Oncology|September 9, 2021
Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine LearningJaewoong Lee, Sungmin Cho, Seong-Eui Hong, et al.
Pageof 3

Showing results (11-20 of 22) with videos related to

Sort By:
Pageof 3
Biotechnology Journal|March 6, 2008
Identification of mouse heart transcriptomic network sensitive to various heart diseasesSeong-Eui Hong, Inju Park, Hyeseon Cha, et al.
The International Journal of Biochemistry & Cell Biology|June 17, 2014
Sumoylation regulates ER stress response by modulating calreticulin gene expression in XBP-1-dependent mode in Caenorhabditis elegansYunki Lim, Dukgyu Lee, Karunambigai Kalichamy, et al.
Plos One|March 14, 2015
miR-185 plays an anti-hypertrophic role in the heart via multiple targets in the calcium-signaling pathwaysJin Ock Kim, Dong Woo Song, Eun Jeong Kwon, et al.
Journal of Molecular and Cellular Cardiology|June 30, 2007
Comprehensive identification and characterization of novel cardiac genes in mouseInju Park, Seong-Eui Hong, Tae-Wan Kim, et al.
Scientific Reports|January 31, 2024
DEEPOMICS FFPE, a deep neural network model, identifies DNA sequencing artifacts from formalin fixed paraffin embedded tissue with high accuracyDong-Hyuk Heo, Inyoung Kim, Heejae Seo, et al.
Oncology Reports|November 1, 2013
SC35 promotes splicing of the C5-V6-C6 isoform of CD44 pre-mRNATiing Jen Loh, Heegyum Moon, Sunghee Cho, et al.
Age (Dordrecht, Netherlands)|July 26, 2012
Identification of the dichotomous role of age-related LCK in calorie restriction revealed by integrative analysis of cDNA microarray and interactomeDaeui Park, Eun Kyeong Lee, Eun Jee Jang, et al.
Physiological Genomics|March 1, 2007
Gene profiling during regression of pressure overload-induced cardiac hypertrophyDong Kwon Yang, Bo Youn Choi, Young-Hoon Lee, et al.
Age (Dordrecht, Netherlands)|July 11, 2009
Revealing system-level correlations between aging and calorie restriction using a mouse transcriptomeSeong-Eui Hong, Hyoung-Sam Heo, Dae Hyun Kim, et al.
Frontiers in Oncology|September 9, 2021
Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine LearningJaewoong Lee, Sungmin Cho, Seong-Eui Hong, et al.
Pageof 3