Y
Young Jin Suh
Researcher at Kyungpook National University
Publications - 395
Citations - 5032
Young Jin Suh is an academic researcher from Kyungpook National University. The author has contributed to research in topics: Ricci curvature & Jacobi operator. The author has an hindex of 34, co-authored 364 publications receiving 4180 citations. Previous affiliations of Young Jin Suh include UPRRP College of Natural Sciences & St. Vincent's Health System.
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Diffusion‐weighted imaging of breast cancer: Correlation of the apparent diffusion coefficient value with prognostic factors
Sung Hun Kim,Eun Suk Cha,Hyeon Sook Kim,Bong Joo Kang,Jae Jeong Choi,Ji Han Jung,Yong Gyu Park,Young Jin Suh +7 more
TL;DR: To evaluate the role of diffusion‐weighted imaging (DWI) in the detection of breast cancers, and to correlate the apparent diffusion coefficient (ADC) value with prognostic factors, a large number of patients with known breast cancers are treated with WI.
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Real Hypersurfaces in Complex Two-Plane Grassmannians
Jurgen Berndt,Young Jin Suh +1 more
TL;DR: In this paper, the complex two-plane Grassmannian with both a Kahler and a quaternionic Kahler structure was applied to the normal bundle of a real hypersurface M in G
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Real hypersurfaces with isometric Reeb flow in complex two-plane Grassmannians
Jurgen Berndt,Young Jin Suh +1 more
TL;DR: In this article, the authors classify real hypersurfaces with isometric Reeb flow in the complex Grassmann manifold G 2 (ℂ istg m+2 petertodd ) of all 2-dimensional linear subspaces in ℂm+2
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Real hypersurfaces of quaternionic projective space satisfying ▽UiR = 0
Juan de Dios Pérez,Young Jin Suh +1 more
TL;DR: In this paper, the authors classify real hypersurfaces of quaternionic projective space whose curvature tensor is parallel in the direction of a 3D distribution, and they show that there are real hypersurifaces with parallel curvature vectors in quaternion projective spaces.
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Prognostic value of systemic inflammatory markers and development of a nomogram in breast cancer
TL;DR: An elevated preoperative PLR is superior to the NLR, dNLR, and LMR in predicting clinical outcomes in patients with breast cancer and the nomogram incorporating the PLR could accurately predict individualized survival probability in breast cancer.