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Sangseon Lee

Showing results (11-20 of 38) with videos related to

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IEEE Transactions on Computational Biology and Bioinformatics|January 14, 2026
EnsDTI: Predicting Drug-Target Interaction with Mixture-of-Experts and Confidence AssessmentYijingxiu Lu, Soosung Kang, Sun Kim, et al.
IEEE Journal of Biomedical and Health Informatics|January 19, 2024
Dual Representation Learning for Predicting Drug-Side Effect Frequency Using Protein Target InformationSungjoon Park, Sangseon Lee, Minwoo Pak, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics|September 9, 2019
Ranked k-Spectrum Kernel for Comparative and Evolutionary Comparison of Exons, Introns, and CpG IslandsSangseon Lee, Taeheon Lee, Yung-Kyun Noh, et al.
BMC Systems Biology|February 4, 2017
Subtype-specific CpG island shore methylation and mutation patterns in 30 breast cancer cell linesHeejoon Chae, Sangseon Lee, Kenneth P Nephew, et al.
Bioinformatics (Oxford, England)|March 25, 2020
Cancer subtype classification and modeling by pathway attention and propagationSangseon Lee, Sangsoo Lim, Taeheon Lee, et al.
Briefings in Bioinformatics|November 22, 2018
Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer dataSangsoo Lim, Sangseon Lee, Inuk Jung, et al.
Briefings in Bioinformatics|February 8, 2023
Improved drug response prediction by drug target data integration via network-based profilingMinwoo Pak, Sangseon Lee, Inyoung Sung, et al.
Scientific Reports|October 3, 2024
ChemAP: predicting drug approval with chemical structures before clinical trial phase by leveraging multi-modal embedding space and knowledge distillationChangyun Cho, Sangseon Lee, Dongmin Bang, et al.
BMC Bioinformatics|April 26, 2022
AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learningInyoung Sung, Sangseon Lee, Minwoo Pak, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics|March 22, 2021
MLDEG: A Machine Learning Approach to Identify Differentially Expressed Genes Using Network Property and Network PropagationJi Hwan Moon, Sangseon Lee, Minwoo Pak, et al.
Pageof 4

Showing results (11-20 of 38) with videos related to

Sort By:
Pageof 4
IEEE Transactions on Computational Biology and Bioinformatics|January 14, 2026
EnsDTI: Predicting Drug-Target Interaction with Mixture-of-Experts and Confidence AssessmentYijingxiu Lu, Soosung Kang, Sun Kim, et al.
IEEE Journal of Biomedical and Health Informatics|January 19, 2024
Dual Representation Learning for Predicting Drug-Side Effect Frequency Using Protein Target InformationSungjoon Park, Sangseon Lee, Minwoo Pak, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics|September 9, 2019
Ranked k-Spectrum Kernel for Comparative and Evolutionary Comparison of Exons, Introns, and CpG IslandsSangseon Lee, Taeheon Lee, Yung-Kyun Noh, et al.
BMC Systems Biology|February 4, 2017
Subtype-specific CpG island shore methylation and mutation patterns in 30 breast cancer cell linesHeejoon Chae, Sangseon Lee, Kenneth P Nephew, et al.
Bioinformatics (Oxford, England)|March 25, 2020
Cancer subtype classification and modeling by pathway attention and propagationSangseon Lee, Sangsoo Lim, Taeheon Lee, et al.
Briefings in Bioinformatics|November 22, 2018
Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer dataSangsoo Lim, Sangseon Lee, Inuk Jung, et al.
Briefings in Bioinformatics|February 8, 2023
Improved drug response prediction by drug target data integration via network-based profilingMinwoo Pak, Sangseon Lee, Inyoung Sung, et al.
Scientific Reports|October 3, 2024
ChemAP: predicting drug approval with chemical structures before clinical trial phase by leveraging multi-modal embedding space and knowledge distillationChangyun Cho, Sangseon Lee, Dongmin Bang, et al.
BMC Bioinformatics|April 26, 2022
AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learningInyoung Sung, Sangseon Lee, Minwoo Pak, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics|March 22, 2021
MLDEG: A Machine Learning Approach to Identify Differentially Expressed Genes Using Network Property and Network PropagationJi Hwan Moon, Sangseon Lee, Minwoo Pak, et al.
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