J. C. Jeong, I. Hands, J. M. Kolesar, M. Rao, B. Davis, Y. Dobyns, J. Hurt-Mueller, J. Levens, J. Gregory, J. Williams, L. Witt, Eun-Mi Kim, C. Burton, A. A. Elbiheary, M. Chang, E. B. Durbin. Local data commons: the sleeping beauty in the community of data commons. BMC Bioinformatics 2022 (printing)
Eun-Mi Kim and Jong Cheol Jeong, Cancer progonosis prediction with Artificial Intelligence and sequence-based Protein-Protein Interaction (seqPPI), TBA, 2020(in progress, https://github.com/eunmikimlab/PPI)
Eun-Mi Kim and Bae-Ho Lee: Efficiency of having Motif in Big-Data, ITII 2018
Eun–Mi Kim, Jong Cheol Jeong, Bae–Ho Lee: New RMF Method for analysis human proteins, KCI 2018
Eun–Mi Kim, Jong Cheol Jeong, Ho–Young Pae, Bae–Ho Lee: A New Feature Selection Method for Improving the Precision of Diagnosing Abnormal Protein Sequences by Support Vector Machine and Vectorization Method. ICANNGA (2) 2007: 364–372
Eun–Mi Kim, Jong Cheol Jeong, Bae–Ho Lee: A New Approach for Finding an Optimal Solution and Regularization by Learning Dynamic Momentum. ICAISC 2006: 29–36
Eun-Mi Kim, Bae-Ho Lee: An Effective Real Time Update Rule for Improving Performances both the Classification and Regression Problems in Kernel Methods. ICIS 2005
Seong-Mi Park, Eun-Mi Kim, Bae-Ho Lee: Computed-Aided Diagnosis System for Detection of Liver Tumor in CT Liver Image. CARS 2005:1404
Eun-Mi Kim, Seong-Mi Park, Kwang-Hee Kim, Bae-Ho Lee: An Effective Machine Learning Algorithm using Momentum Scheduling. HIS 2004:442-443
