연구성과로 돌아가기

2025 연구성과별 연구자 정보 (116 / 1187)

※ 현재 Web of Science 저자 정보만 집계되어 있습니다.
※ 컨트롤 + 클릭으로 열별 다중 정렬 가능합니다.
Excel 다운로드
Document Title Author Full Name Author Short Name Index Corresponding Address ResearcherID ResearcherID Author Name ORCID ORCID Author Name Related Email
Auto-sumoylation of the yeast Ubc9 E2 SUMO-conjugating enzyme extends cellular lifespan Zhao, Dejian Zhao, DJ 5 Yale Univ, Yale Ctr Genome Anal, New Haven, CT USA mark.hochstrasser@yale.edu; rhr4757@knu.ac.kr;
Auto-sumoylation of the yeast Ubc9 E2 SUMO-conjugating enzyme extends cellular lifespan Knight, James Knight, J 6 Yale Univ, Yale Ctr Genome Anal, New Haven, CT USA mark.hochstrasser@yale.edu; rhr4757@knu.ac.kr;
Auto-sumoylation of the yeast Ubc9 E2 SUMO-conjugating enzyme extends cellular lifespan Lam, TuKiet T. Lam, TT 7 Yale Sch Med, Keck MS & Proteom Resource, New Haven, CT USA B-6343-2008 Lam, TuKiet mark.hochstrasser@yale.edu; rhr4757@knu.ac.kr;
Auto-sumoylation of the yeast Ubc9 E2 SUMO-conjugating enzyme extends cellular lifespan Jin, Jong Hwa Jin, JH 8 Osong Med Innovat Fdn, New Drug Dev Ctr, Cheongju, South Korea mark.hochstrasser@yale.edu; rhr4757@knu.ac.kr;
Auto-sumoylation of the yeast Ubc9 E2 SUMO-conjugating enzyme extends cellular lifespan Lee, Hyun-Shik Lee, HS 9 Kyungpook Natl Univ, KNU Inst Basic Sci, Coll Nat Sci, KNU G LAMP Res Ctr,Sch Life Sci,BK21 FOUR KNU Cre, Daegu, South Korea mark.hochstrasser@yale.edu; rhr4757@knu.ac.kr;
Auto-sumoylation of the yeast Ubc9 E2 SUMO-conjugating enzyme extends cellular lifespan Hochstrasser, Mark Hochstrasser, M 10 교신저자 Yale Univ, Dept Mol Biophys & Biochem, New Haven, CT 06520 USA mark.hochstrasser@yale.edu; rhr4757@knu.ac.kr;
Auto-sumoylation of the yeast Ubc9 E2 SUMO-conjugating enzyme extends cellular lifespan Ryu, Hong-Yeoul Ryu, HY 11 교신저자 Kyungpook Natl Univ, KNU Inst Basic Sci, Coll Nat Sci, KNU G LAMP Res Ctr,Sch Life Sci,BK21 FOUR KNU Cre, Daegu, South Korea mark.hochstrasser@yale.edu; rhr4757@knu.ac.kr;
Automated interpretation of cardiotocography using deep learning in a nationwide multicenter study Park, Chang Eun Park, CE 1 Ajou Univ, Grad Sch Med, Dept Convergence Healthcare Med, Suwon, South Korea AEW-4266-2022 Park, Chang-Eun bg9523@ajou.ac.kr; choi328328@ajou.ac.kr; veritas@ajou.ac.kr; kdw1015@gmail.com; mongkoko@catholic.ac.kr; wjseong@knu.ac.kr; chh9861@knu.ac.kr; hyunmik@gmail.com; vmariagnes@gmail.com; seolhj@khu.ac.kr; pyun0522@gmail.com; novak082@naver.com; yundan76@dankook.ac.kr; kjohmd@snubh.org; jsparkmd@snu.ac.kr; ob.youngnam@gmail.com; camanbal@paik.ac.kr; kimyh@chonnam.ac.kr; gjkim@cau.ac.kr; kmr5300@ajou.ac.kr; zzanga-94@hanmail.net;
Automated interpretation of cardiotocography using deep learning in a nationwide multicenter study Choi, Byungjin Choi, B 2 Ajou Univ, Grad Sch Med, Dept Biomed Informat, Suwon, South Korea bg9523@ajou.ac.kr; choi328328@ajou.ac.kr; veritas@ajou.ac.kr; kdw1015@gmail.com; mongkoko@catholic.ac.kr; wjseong@knu.ac.kr; chh9861@knu.ac.kr; hyunmik@gmail.com; vmariagnes@gmail.com; seolhj@khu.ac.kr; pyun0522@gmail.com; novak082@naver.com; yundan76@dankook.ac.kr; kjohmd@snubh.org; jsparkmd@snu.ac.kr; ob.youngnam@gmail.com; camanbal@paik.ac.kr; kimyh@chonnam.ac.kr; gjkim@cau.ac.kr; kmr5300@ajou.ac.kr; zzanga-94@hanmail.net;
Automated interpretation of cardiotocography using deep learning in a nationwide multicenter study Choi, Byungjin Choi, B 2 Jeju Natl Univ Hosp, Jeju, South Korea bg9523@ajou.ac.kr; choi328328@ajou.ac.kr; veritas@ajou.ac.kr; kdw1015@gmail.com; mongkoko@catholic.ac.kr; wjseong@knu.ac.kr; chh9861@knu.ac.kr; hyunmik@gmail.com; vmariagnes@gmail.com; seolhj@khu.ac.kr; pyun0522@gmail.com; novak082@naver.com; yundan76@dankook.ac.kr; kjohmd@snubh.org; jsparkmd@snu.ac.kr; ob.youngnam@gmail.com; camanbal@paik.ac.kr; kimyh@chonnam.ac.kr; gjkim@cau.ac.kr; kmr5300@ajou.ac.kr; zzanga-94@hanmail.net;
Automated interpretation of cardiotocography using deep learning in a nationwide multicenter study Park, Rae Woong Park, RW 3 Ajou Univ, Grad Sch Med, Dept Biomed Informat, Suwon, South Korea bg9523@ajou.ac.kr; choi328328@ajou.ac.kr; veritas@ajou.ac.kr; kdw1015@gmail.com; mongkoko@catholic.ac.kr; wjseong@knu.ac.kr; chh9861@knu.ac.kr; hyunmik@gmail.com; vmariagnes@gmail.com; seolhj@khu.ac.kr; pyun0522@gmail.com; novak082@naver.com; yundan76@dankook.ac.kr; kjohmd@snubh.org; jsparkmd@snu.ac.kr; ob.youngnam@gmail.com; camanbal@paik.ac.kr; kimyh@chonnam.ac.kr; gjkim@cau.ac.kr; kmr5300@ajou.ac.kr; zzanga-94@hanmail.net;
Automated interpretation of cardiotocography using deep learning in a nationwide multicenter study Kwak, Dong Wook Kwak, DW 4 Ajou Univ, Sch Med, Dept Obstet & Gynecol, Suwon, South Korea bg9523@ajou.ac.kr; choi328328@ajou.ac.kr; veritas@ajou.ac.kr; kdw1015@gmail.com; mongkoko@catholic.ac.kr; wjseong@knu.ac.kr; chh9861@knu.ac.kr; hyunmik@gmail.com; vmariagnes@gmail.com; seolhj@khu.ac.kr; pyun0522@gmail.com; novak082@naver.com; yundan76@dankook.ac.kr; kjohmd@snubh.org; jsparkmd@snu.ac.kr; ob.youngnam@gmail.com; camanbal@paik.ac.kr; kimyh@chonnam.ac.kr; gjkim@cau.ac.kr; kmr5300@ajou.ac.kr; zzanga-94@hanmail.net;
Automated interpretation of cardiotocography using deep learning in a nationwide multicenter study Ko, Hyun Sun Ko, HS 5 Catholic Univ Korea, Seoul St Marys Hosp, Coll Med, Dept Obstet & Gynecol, Seoul, South Korea bg9523@ajou.ac.kr; choi328328@ajou.ac.kr; veritas@ajou.ac.kr; kdw1015@gmail.com; mongkoko@catholic.ac.kr; wjseong@knu.ac.kr; chh9861@knu.ac.kr; hyunmik@gmail.com; vmariagnes@gmail.com; seolhj@khu.ac.kr; pyun0522@gmail.com; novak082@naver.com; yundan76@dankook.ac.kr; kjohmd@snubh.org; jsparkmd@snu.ac.kr; ob.youngnam@gmail.com; camanbal@paik.ac.kr; kimyh@chonnam.ac.kr; gjkim@cau.ac.kr; kmr5300@ajou.ac.kr; zzanga-94@hanmail.net;
Automated interpretation of cardiotocography using deep learning in a nationwide multicenter study Seong, Won Joon Seong, WJ 6 Kyungpook Natl Univ, Sch Med, Dept Obstet & Gynecol, Daegu, South Korea bg9523@ajou.ac.kr; choi328328@ajou.ac.kr; veritas@ajou.ac.kr; kdw1015@gmail.com; mongkoko@catholic.ac.kr; wjseong@knu.ac.kr; chh9861@knu.ac.kr; hyunmik@gmail.com; vmariagnes@gmail.com; seolhj@khu.ac.kr; pyun0522@gmail.com; novak082@naver.com; yundan76@dankook.ac.kr; kjohmd@snubh.org; jsparkmd@snu.ac.kr; ob.youngnam@gmail.com; camanbal@paik.ac.kr; kimyh@chonnam.ac.kr; gjkim@cau.ac.kr; kmr5300@ajou.ac.kr; zzanga-94@hanmail.net;
Automated interpretation of cardiotocography using deep learning in a nationwide multicenter study Cha, Hyun-Hwa Cha, HH 7 Kyungpook Natl Univ, Sch Med, Dept Obstet & Gynecol, Daegu, South Korea bg9523@ajou.ac.kr; choi328328@ajou.ac.kr; veritas@ajou.ac.kr; kdw1015@gmail.com; mongkoko@catholic.ac.kr; wjseong@knu.ac.kr; chh9861@knu.ac.kr; hyunmik@gmail.com; vmariagnes@gmail.com; seolhj@khu.ac.kr; pyun0522@gmail.com; novak082@naver.com; yundan76@dankook.ac.kr; kjohmd@snubh.org; jsparkmd@snu.ac.kr; ob.youngnam@gmail.com; camanbal@paik.ac.kr; kimyh@chonnam.ac.kr; gjkim@cau.ac.kr; kmr5300@ajou.ac.kr; zzanga-94@hanmail.net;
페이지 이동: