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Doyeon Ha

Showing results (1-10 of 8) with videos related to

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Briefings in Bioinformatics|December 27, 2022
An evolution-based machine learning to identify cancer type-specific driver mutationsDonghyo Kim, Doyeon Ha, Kwanghwan Lee, et al.
Nucleic Acids Research|February 9, 2022
Evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genesDoyeon Ha, Donghyo Kim, Inhae Kim, et al.
Cell Reports Methods|April 18, 2026
A network-based deep learning model integrating subclonal architecture for therapy response prediction in cancerSungnam Kim, Doyeon Ha, A-Reum Nam, et al.
Nature Communications|October 31, 2020
Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patientsJungHo Kong, Heetak Lee, Donghyo Kim, et al.
Nature Communications|June 28, 2022
Network-based machine learning approach to predict immunotherapy response in cancer patientsJungHo Kong, Doyeon Ha, Juhun Lee, et al.
BMB Reports|October 26, 2022
Development of bioinformatics and multi-omics analyses in organoidsDoyeon Ha, JungHo Kong, Donghyo Kim, et al.
Science Advances|January 31, 2024
Cell-cell communication network-based interpretable machine learning predicts cancer patient response to immune checkpoint inhibitorsJuhun Lee, Donghyo Kim, JungHo Kong, et al.
Genomics, Proteomics & Bioinformatics|September 26, 2025
A Co-essentiality Network of Cancer Driver Genes Better Prioritizes Anticancer DrugsKwanghwan Lee, Donghyo Kim, Inhae Kim, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
Briefings in Bioinformatics|December 27, 2022
An evolution-based machine learning to identify cancer type-specific driver mutationsDonghyo Kim, Doyeon Ha, Kwanghwan Lee, et al.
Nucleic Acids Research|February 9, 2022
Evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genesDoyeon Ha, Donghyo Kim, Inhae Kim, et al.
Cell Reports Methods|April 18, 2026
A network-based deep learning model integrating subclonal architecture for therapy response prediction in cancerSungnam Kim, Doyeon Ha, A-Reum Nam, et al.
Nature Communications|October 31, 2020
Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patientsJungHo Kong, Heetak Lee, Donghyo Kim, et al.
Nature Communications|June 28, 2022
Network-based machine learning approach to predict immunotherapy response in cancer patientsJungHo Kong, Doyeon Ha, Juhun Lee, et al.
BMB Reports|October 26, 2022
Development of bioinformatics and multi-omics analyses in organoidsDoyeon Ha, JungHo Kong, Donghyo Kim, et al.
Science Advances|January 31, 2024
Cell-cell communication network-based interpretable machine learning predicts cancer patient response to immune checkpoint inhibitorsJuhun Lee, Donghyo Kim, JungHo Kong, et al.
Genomics, Proteomics & Bioinformatics|September 26, 2025
A Co-essentiality Network of Cancer Driver Genes Better Prioritizes Anticancer DrugsKwanghwan Lee, Donghyo Kim, Inhae Kim, et al.
Pageof 1