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Inyoung Sung

Showing results (1-10 of 12) with videos related to

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Briefings in Bioinformatics|November 25, 2025
Transcriptome Transformer: improving patient survival prediction via multitask learning of transcriptomic and clinical featuresBonil Koo, Inyoung Sung, Sangseon Lee, et al.
NPJ Precision Oncology|May 20, 2026
Deep learning for predicting patient drug response by transferring gene-level and cell-level knowledge to tumorsInyoung Sung, Dongmin Bang, Sun Kim, et al.
Frontiers in Genetics|October 7, 2024
DGDRP: drug-specific gene selection for drug response prediction via re-ranking through propagating and learning biological networkMinwoo Pak, Dongmin Bang, Inyoung Sung, 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.
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.
Scientific Reports|June 16, 2021
Inferring transcriptomic cell states and transitions only from time series transcriptome dataKyuri Jo, Inyoung Sung, Dohoon Lee, 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.
Cancers|September 9, 2022
Risk Stratification for Breast Cancer Patient by Simultaneous Learning of Molecular Subtype and Survival Outcome Using Genetic Algorithm-Based Gene Set SelectionBonil Koo, Dohoon Lee, Sangseon Lee, et al.
Frontiers in Pharmacology|February 10, 2025
MDTR: a knowledge-guided interpretable representation for quantifying liver toxicity at transcriptomic levelInyoung Sung, Sangseon Lee, Dongmin Bang, et al.
Computational and Structural Biotechnology Journal|April 12, 2021
A review on compound-protein interaction prediction methods: Data, format, representation and modelSangsoo Lim, Yijingxiu Lu, Chang Yun Cho, et al.
Pageof 2

Showing results (1-10 of 12) with videos related to

Sort By:
Pageof 2
Briefings in Bioinformatics|November 25, 2025
Transcriptome Transformer: improving patient survival prediction via multitask learning of transcriptomic and clinical featuresBonil Koo, Inyoung Sung, Sangseon Lee, et al.
NPJ Precision Oncology|May 20, 2026
Deep learning for predicting patient drug response by transferring gene-level and cell-level knowledge to tumorsInyoung Sung, Dongmin Bang, Sun Kim, et al.
Frontiers in Genetics|October 7, 2024
DGDRP: drug-specific gene selection for drug response prediction via re-ranking through propagating and learning biological networkMinwoo Pak, Dongmin Bang, Inyoung Sung, 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.
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.
Scientific Reports|June 16, 2021
Inferring transcriptomic cell states and transitions only from time series transcriptome dataKyuri Jo, Inyoung Sung, Dohoon Lee, 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.
Cancers|September 9, 2022
Risk Stratification for Breast Cancer Patient by Simultaneous Learning of Molecular Subtype and Survival Outcome Using Genetic Algorithm-Based Gene Set SelectionBonil Koo, Dohoon Lee, Sangseon Lee, et al.
Frontiers in Pharmacology|February 10, 2025
MDTR: a knowledge-guided interpretable representation for quantifying liver toxicity at transcriptomic levelInyoung Sung, Sangseon Lee, Dongmin Bang, et al.
Computational and Structural Biotechnology Journal|April 12, 2021
A review on compound-protein interaction prediction methods: Data, format, representation and modelSangsoo Lim, Yijingxiu Lu, Chang Yun Cho, et al.
Pageof 2