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Nanotechnology|October 14, 2009
Silicon nanowire-based tunneling field-effect transistors on flexible plastic substratesMyeongwon Lee, Jamin Koo, Eun-Ae Chung, et al.
Nanotechnology|August 12, 2011
ZnO nanowire-based nano-floating gate memory with Pt nanocrystals embedded in Al(2)O(3) gate oxidesDonghyuk Yeom, Jeongmin Kang, Myoungwon Lee, et al.
Frontiers in Immunology|December 12, 2025
Machine learning-based prediction of response to Janus kinase inhibitors in patients with rheumatoid arthritis using clinical dataYeo-Jin Lee, Gyucheol Choi, Joongyeub Yeo, et al.
Veterinary Sciences|August 28, 2024
Prognostic Utility of the Flow Cytometry and Clonality Analysis Results for Feline LymphomasSheena Kapoor, Sushmita Sen, Josephine Tsang, et al.
JCO Clinical Cancer Informatics|December 2, 2025
Predicting Chemotherapy Response in Patients With Advanced or Metastatic Pancreatic Cancer Using Machine LearningJamin Koo, Gyucheol Choi, Jaekyung Cheon, et al.
NPJ Precision Oncology|June 12, 2025
Identification of novel genetic mutations for the treatment prognostication of canine lymphomaJosephine Tsang, Qi Jing Yap, Sheena Kapoor, et al.
Haematologica|September 14, 2023
Prognostic value of European LeukemiaNet 2022 criteria and genomic clusters using machine learning in older adults with acute myeloid leukemiaSilvia Park, Tong Yoon Kim, Byung-Sik Cho, et al.
NPJ Precision Oncology|May 20, 2023
ML-based sequential analysis to assist selection between VMP and RD for newly diagnosed multiple myelomaSung-Soo Park, Jong Cheol Lee, Ja Min Byun, et al.
Veterinary Sciences|December 23, 2021
Predicting Dynamic Clinical Outcomes of the Chemotherapy for Canine Lymphoma Patients Using a Machine Learning ModelJamin Koo, Kyucheol Choi, Peter Lee, et al.
Veterinary and Comparative Oncology|October 7, 2020
Predicting likelihood of in vivo chemotherapy response in canine lymphoma using ex vivo drug sensitivity and immunophenotyping data in a machine learning modelZach Bohannan, Raghavendra Sumanth Pudupakam, Jamin Koo, et al.
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