publications

2023

1. Deep learning-based survival prediction using DNA methylation-derived 3D genomic information

   Yang, Jeewon, Lee, Dohoon, Koo, Bonil, and Kim, Sun

   bioRxiv 2023

   Bib HTML

   @article{yang2023deep,
     title = {Deep learning-based survival prediction using DNA methylation-derived 3D genomic information},
     author = {Yang, Jeewon and Lee, Dohoon and Koo, Bonil and Kim, Sun},
     journal = {bioRxiv},
     doi = {10.1101/2023.06.10.544450},
     publisher = {Cold Spring Harbor Laboratory},
     year = {2023},
     html = {https://www.biorxiv.org/content/10.1101/2023.06.10.544450.abstract},
     bibtex_show = {true},
     selected = {false}
   }

2. Evaluation and optimization of sequence-based gene regulatory deep learning models

   Rafi, Abdul Muntakim, Penzar, Dmitry, Nogina, Daria, Lee, Dohoon, Vaishnav, Eeshit Dhaval, Lee, Danyeong, Kim, Nayeon, Kim, Sangyeup, Meshcheryakov, Georgy, Lando, Andrey, and others,

   bioRxiv 2023

   Bib HTML

   @article{rafi2023evaluation,
     title = {Evaluation and optimization of sequence-based gene regulatory deep learning models},
     author = {Rafi, Abdul Muntakim and Penzar, Dmitry and Nogina, Daria and Lee, Dohoon and Vaishnav, Eeshit Dhaval and Lee, Danyeong and Kim, Nayeon and Kim, Sangyeup and Meshcheryakov, Georgy and Lando, Andrey and others},
     journal = {bioRxiv},
     doi = {10.1101/2023.04.26.538471},
     publisher = {Cold Spring Harbor Laboratory},
     year = {2023},
     html = {https://www.biorxiv.org/content/10.1101/2023.04.26.538471v1.abstract},
     bibtex_show = {true},
     selected = {false}
   }

3. AMLs harboring DNMT3A-destabilizing variants show increased intratumor DNA methylation heterogeneity at bivalent chromatin domains

   Lee, Dohoon, Koo, Bonil, Kim, Seok-Hyun, Byun, Jamin, Hong, Junshik, Shin, Dong-Yeop, Sun, Choong-Hyun, Song, Ji-Joon, Kim, Jaesung, Jaiswal, Siddhartha, Yoon, Sung-soo, Kim, Sun, and Koh, Youngil

   bioRxiv 2023

   Bib HTML

   @article{lee2023dnmt3a,
     author = {Lee, Dohoon and Koo, Bonil and Kim, Seok-Hyun and Byun, Jamin and Hong, Junshik and Shin, Dong-Yeop and Sun, Choong-Hyun and Song, Ji-Joon and Kim, Jaesung and Jaiswal, Siddhartha and Yoon, Sung-soo and Kim, Sun and Koh, Youngil},
     title = {AMLs harboring DNMT3A-destabilizing variants show increased intratumor DNA methylation heterogeneity at bivalent chromatin domains},
     journal = {bioRxiv},
     doi = {10.1101/2023.02.13.528223},
     publisher = {Cold Spring Harbor Laboratory},
     year = {2023},
     elocation-id = {2023.02.13.528223},
     html = {https://www.biorxiv.org/content/early/2023/02/14/2023.02.13.528223},
     bibtex_show = {true},
     selected = {true}
   }

2022

1. Learning the histone codes with large genomic windows and three-dimensional chromatin interactions using transformer

   Lee, Dohoon, Yang, Jeewon, and Kim, Sun

   Nature Communications 2022

   Bib HTML Code Presentation

   @article{lee2022chromoformer,
     author = {Lee, Dohoon and Yang, Jeewon and Kim, Sun},
     title = {Learning the histone codes with large genomic windows and three-dimensional chromatin interactions using transformer},
     journal = {Nature Communications},
     doi = {10.1038/s41467-022-34152-5},
     publisher = {Springer Nature},
     year = {2022},
     code = {https://github.com/dohlee/chromoformer},
     html = {https://www.nature.com/articles/s41467-022-34152-5},
     presentation = {https://youtu.be/PfKabJ7iHJo},
     bibtex_show = {true},
     selected = {true}
   }

2. Single-cell transcriptome analyses reveal distinct gene expression signatures of severe COVID-19 in the presence of clonal hematopoiesis

   Choi, Baekgyu, Kang, Chang Kyung, Park, Seongwan, Lee, Dohoon, Lee, Andrew J., Ko, Yuji, Kang, Suk-Jo, Kang, Kyuho, Kim, Sun, Koh, Youngil, and Jung, Inkyung

   Experimental and Molecular Medicine 2022

   Bib HTML

   @article{choi2022clonal,
     author = {Choi, Baekgyu and Kang, Chang Kyung and Park, Seongwan and Lee, Dohoon and Lee, Andrew J. and Ko, Yuji and Kang, Suk-Jo and Kang, Kyuho and Kim, Sun and Koh, Youngil and Jung, Inkyung},
     bibtex_show = {true},
     title = {Single-cell transcriptome analyses reveal distinct gene expression signatures of severe COVID-19 in the presence of clonal hematopoiesis},
     journal = {Experimental and Molecular Medicine},
     doi = {10.1038/s12276-022-00866-1},
     publisher = {Nature Publishing Group},
     year = {2022},
     html = {https://www.nature.com/articles/s12276-022-00866-1}
   }

3. Metheor: Ultrafast DNA methylation heterogeneity calculation from bisulfite read alignments

   Lee, Dohoon, Koo, Bonil, Yang, Jeewon, and Kim, Sun

   bioRxiv 2022

   Bib HTML Code

   @article{lee2022metheor,
     author = {Lee, Dohoon and Koo, Bonil and Yang, Jeewon and Kim, Sun},
     title = {Metheor: Ultrafast DNA methylation heterogeneity calculation from bisulfite read alignments},
     journal = {bioRxiv},
     doi = {10.1101/2022.07.20.500893},
     publisher = {Cold Spring Harbor Laboratory},
     year = {2022},
     code = {https://github.com/dohlee/metheor},
     html = {https://www.biorxiv.org/content/10.1101/2022.07.20.500893v1},
     bibtex_show = {true}
   }

4. Risk Stratification for Breast Cancer Patient by Simultaneous Learning of Molecular Subtype and Survival Outcome Using Genetic Algorithm-Based Gene Set Selection

   Koo, Bonil, Lee, Dohoon, Lee, Sangseon, Sung, Inyoung, Kim, Sun, and Lee, Sunho

   Cancers 2022

   Bib HTML Code

   @article{koo2022risk,
     author = {Koo, Bonil and Lee, Dohoon and Lee, Sangseon and Sung, Inyoung and Kim, Sun and Lee, Sunho},
     title = {Risk Stratification for Breast Cancer Patient by Simultaneous Learning of Molecular Subtype and Survival Outcome Using Genetic Algorithm-Based Gene Set Selection},
     journal = {Cancers},
     year = {2022},
     code = {https://github.com/dohlee/adipogenesis-methylation},
     html = {https://www.mdpi.com/2072-6694/14/17/4120},
     bibtex_show = {true}
   }

5. Targeted erasure of DNA methylation by TET3 drives adipogenic reprogramming and differentiation

   Park, Jeu, Lee, Dohoon, Ham, Seokjin, Oh, Jiyoung, Noh, Jung-Ran, Lee, Yun Kyung, Park, Yoon Jeong, Lee, Gung, Han, Sang Mun, Han, Ji Seul, Kim, Ye Young, Jeon, Yong Geun, Nahmgoong, Han, Shin, Kyung Cheul, Kim, Sung Min, Choi, Sung Hee, Lee, Chul-Ho, Park, Jiyoung, Roh, Tae Young, Kim, Sun, and Kim, Jae Bum

   Nature Metabolism 2022

   Bib HTML

   @article{park2022tet3,
     title = {Targeted erasure of DNA methylation by TET3 drives adipogenic reprogramming and differentiation},
     author = {Park, Jeu and Lee, Dohoon and Ham, Seokjin and Oh, Jiyoung and Noh, Jung-Ran and Lee, Yun Kyung and Park, Yoon Jeong and Lee, Gung and Han, Sang Mun and Han, Ji Seul and Kim, Ye Young and Jeon, Yong Geun and Nahmgoong, Han and Shin, Kyung Cheul and Kim, Sung Min and Choi, Sung Hee and Lee, Chul-Ho and Park, Jiyoung and Roh, Tae Young and Kim, Sun and Kim, Jae Bum},
     journal = {Nature Metabolism},
     publisher = {Nature Publishing Group},
     year = {2022},
     html = {https://www.nature.com/articles/s42255-022-00597-7},
     bibtex_show = {true}
   }

6. Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification

   Piao, Yinhua, Lee, Sangseon, Lee, Dohoon, and Kim, Sun

   Proceedings of the AAAI conference on artificial intelligence 2022

   arXiv Bib

   @article{piao2021sparse,
     bibtex_show = {true},
     title = {Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification},
     author = {Piao, Yinhua and Lee, Sangseon and Lee, Dohoon and Kim, Sun},
     journal = {Proceedings of the AAAI conference on artificial intelligence},
     arxiv = {2112.06386},
     year = {2022}
   }

2021

1. Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches

   Lee, Dohoon, Park, Youngjune, and Kim, Sun

   Briefings in bioinformatics 2021

   Bib HTML PDF

   @article{lee2021towards,
     title = {Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches},
     author = {Lee, Dohoon and Park, Youngjune and Kim, Sun},
     journal = {Briefings in bioinformatics},
     volume = {22},
     number = {3},
     pages = {bbaa188},
     year = {2021},
     publisher = {Oxford University Press},
     html = {https://pubmed.ncbi.nlm.nih.gov/34020548/},
     pdf = {},
     selected = {true},
     bibtex_show = {true}
   }

2. Inferring transcriptomic cell states and transitions only from time series transcriptome data

   Jo, Kyuri, Sung, Inyoung, Lee, Dohoon, Jang, Hyuksoon, and Kim, Sun

   Scientific reports 2021

   Bib HTML

   @article{jo2021inferring,
     bibtex_show = {true},
     title = {Inferring transcriptomic cell states and transitions only from time series transcriptome data},
     author = {Jo, Kyuri and Sung, Inyoung and Lee, Dohoon and Jang, Hyuksoon and Kim, Sun},
     journal = {Scientific reports},
     volume = {11},
     number = {1},
     pages = {1--13},
     year = {2021},
     publisher = {Nature Publishing Group},
     html = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8206345/}
   }

3. BioVLAB-Cancer-Pharmacogenomics: tumor heterogeneity and pharmacogenomics analysis of multi-omics data from tumor on the cloud

   Park, Sungjoon, Lee, Dohoon, Kim, Youngkuk, Lim, Sangsoo, Chae, Heejoon, and Kim, Sun

   Bioinformatics 2021

   Bib HTML

   @article{park2021biovlab,
     bibtex_show = {true},
     title = {BioVLAB-Cancer-Pharmacogenomics: tumor heterogeneity and pharmacogenomics analysis of multi-omics data from tumor on the cloud},
     author = {Park, Sungjoon and Lee, Dohoon and Kim, Youngkuk and Lim, Sangsoo and Chae, Heejoon and Kim, Sun},
     journal = {Bioinformatics},
     year = {2021},
     html = {https://pubmed.ncbi.nlm.nih.gov/34185062/}
   }

4. Knowledge-guided artificial intelligence technologies for decoding complex multiomics interactions in cells

   Lee, Dohoon, and Kim, Sun

   Clinical and experimental pediatrics 2021

   Bib HTML PDF

   @article{lee2021knowledge,
     bibtex_show = {true},
     title = {Knowledge-guided artificial intelligence technologies for decoding complex multiomics interactions in cells},
     author = {Lee, Dohoon and Kim, Sun},
     journal = {Clinical and experimental pediatrics},
     year = {2021},
     html = {https://www.e-cep.org/journal/view.php?doi=10.3345/cep.2021.01438},
     pdf = {https://www.e-cep.org/upload/pdf/cep-2021-01438.pdf}
   }

5. Learning the histone codes of gene regulation with large genomic windows and three-dimensional chromatin interactions using transformer

   Lee, Dohoon, Yang, Jeewon, and Kim, Sun

   bioRxiv 2021

   Bib HTML Code Presentation

   @article{Lee2021.12.30.472333,
     bibtex_show = {true},
     title = {Learning the histone codes of gene regulation with large genomic windows and three-dimensional chromatin interactions using transformer},
     author = {Lee, Dohoon and Yang, Jeewon and Kim, Sun},
     doi = {10.1101/2021.12.30.472333},
     journal = {bioRxiv},
     publisher = {Cold Spring Harbor Laboratory},
     html = {https://www.biorxiv.org/content/early/2021/12/30/2021.12.30.472333},
     code = {https://github.com/dohlee/chromoformer},
     presentation = {https://youtu.be/PfKabJ7iHJo},
     selected = {true},
     year = {2021}
   }

2020

1. DRIM: A web-based system for investigating drug response at the molecular level by condition-specific multi-omics data integration

   Oh, Minsik, Park, Sungjoon, Lee, Sangseon, Lee, Dohoon, Lim, Sangsoo, Jeong, Dabin, Jo, Kyuri, Jung, Inuk, and Kim, Sun

   Frontiers in genetics 2020

   Bib HTML

   @article{oh2020drim,
     bibtex_show = {true},
     title = {DRIM: A web-based system for investigating drug response at the molecular level by condition-specific multi-omics data integration},
     author = {Oh, Minsik and Park, Sungjoon and Lee, Sangseon and Lee, Dohoon and Lim, Sangsoo and Jeong, Dabin and Jo, Kyuri and Jung, Inuk and Kim, Sun},
     journal = {Frontiers in genetics},
     volume = {11},
     year = {2020},
     publisher = {Frontiers},
     html = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689278/}
   }

2. Learning cell-type-specific gene regulation mechanisms by multi-attention based deep learning with regulatory latent space

   Kang, Minji, Lee, Sangseon, Lee, Dohoon, and Kim, Sun

   Frontiers in genetics 2020

   Bib HTML

   @article{kang2020learning,
     bibtex_show = {true},
     title = {Learning cell-type-specific gene regulation mechanisms by multi-attention based deep learning with regulatory latent space},
     author = {Kang, Minji and Lee, Sangseon and Lee, Dohoon and Kim, Sun},
     journal = {Frontiers in genetics},
     volume = {11},
     pages = {869},
     year = {2020},
     publisher = {Frontiers},
     html = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7561362/}
   }

2019

1. ISMB2019

   PRISM: methylation pattern-based, reference-free inference of subclonal makeup

   Lee, Dohoon, Lee, Sangseon, and Kim, Sun

   Bioinformatics 2019

   Bib HTML PDF Presentation

   @article{lee2019prism,
     abbr = {ISMB2019},
     bibtex_show = {true},
     title = {PRISM: methylation pattern-based, reference-free inference of subclonal makeup},
     author = {Lee, Dohoon and Lee, Sangseon and Kim, Sun},
     journal = {Bioinformatics},
     volume = {35},
     number = {14},
     pages = {i520--i529},
     year = {2019},
     publisher = {Oxford University Press},
     html = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6612819},
     pdf = {},
     selected = {true},
     presentation = {https://www.youtube.com/watch?v=Ii8YDjqNbL0}
   }