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Inyong Jeong

Showing results (11-20 of 16) with videos related to

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Scientific Reports|March 27, 2026
Prediction of acute kidney injury in patients with acute pesticide poisoning using the PKIP scoreYounghee Kim, Se-Jin Ahn, Nam-Jun Cho, et al.
Communications Medicine|April 23, 2026
Comprehensive validation of machine learning models predicting chemotherapy related electrolyte disorders in a multicenter studyNam-Jun Cho, Inyong Jeong, Se-Jin Ahn, et al.
Neurosurgical Review|March 26, 2026
Discriminating infectious and non-infectious fever after open craniotomy: a combined statistical and machine learning approachHyo Jeong Kim, Jeonghwan Kim, Jinhoo Seok, et al.
Journal of Affective Disorders|August 24, 2025
Actigraphy-based step analysis for the detection of depressed mood: An explainable machine learning approachJu-Wan Kim, Taeyeong Lee, Bahngtaik Lim, et al.
Journal of Affective Disorders|June 4, 2026
Week-ahead prediction of depressive episodes using wearable-derived circadian biomarkers: A multicenter deep learning study with risk-based operating thresholdsByeongsu Kim, Minsu Chae, Hyungjun Seo, et al.
The Plant Pathology Journal|December 16, 2024
Functional Analysis of KPvRxLR27, a Novel Plasmopara viticola Effector from a Korean Isolate, and Its Role in Hypersensitive ResponseMarc Semunyana, Rahel Dinsa Guta, Guogeng Jia, et al.
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Showing results (11-20 of 16) with videos related to

Sort By:
Pageof 2
You have reached the last page of results.This site can display upto 16 results.
Scientific Reports|March 27, 2026
Prediction of acute kidney injury in patients with acute pesticide poisoning using the PKIP scoreYounghee Kim, Se-Jin Ahn, Nam-Jun Cho, et al.
Communications Medicine|April 23, 2026
Comprehensive validation of machine learning models predicting chemotherapy related electrolyte disorders in a multicenter studyNam-Jun Cho, Inyong Jeong, Se-Jin Ahn, et al.
Neurosurgical Review|March 26, 2026
Discriminating infectious and non-infectious fever after open craniotomy: a combined statistical and machine learning approachHyo Jeong Kim, Jeonghwan Kim, Jinhoo Seok, et al.
Journal of Affective Disorders|August 24, 2025
Actigraphy-based step analysis for the detection of depressed mood: An explainable machine learning approachJu-Wan Kim, Taeyeong Lee, Bahngtaik Lim, et al.
Journal of Affective Disorders|June 4, 2026
Week-ahead prediction of depressive episodes using wearable-derived circadian biomarkers: A multicenter deep learning study with risk-based operating thresholdsByeongsu Kim, Minsu Chae, Hyungjun Seo, et al.
The Plant Pathology Journal|December 16, 2024
Functional Analysis of KPvRxLR27, a Novel Plasmopara viticola Effector from a Korean Isolate, and Its Role in Hypersensitive ResponseMarc Semunyana, Rahel Dinsa Guta, Guogeng Jia, et al.
Pageof 2