CONFERENCES
2025
4.
김채원; 정명현; 김민건; 유선용
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Bioinformatics, Drugs, Transcriptome
@conference{nokey,
title = {Conditional Diffusion Model 기반 약물로 인한 전사체 반응 예측},
author = {김채원 and 정명현 and 김민건 and 유선용},
url = {https://bmil.jnu.ac.kr/wp-content/uploads/2025/07/김채원-Conditional-Diffusion-Model-기반-약물로-인한-전사체-반응-예측.pdf},
year = {2025},
date = {2025-07-04},
urldate = {2025-07-04},
booktitle = {2025 한국디지털콘텐츠학회 하계종합학술대회},
publisher = {한국디지털콘텐츠학회},
abstract = {본 논문에서는 Conditional Diffusion Model 기반 교란 조건을 고려한 전사체 변화 예측 심층 생성 모델을 소개한다 처리한 화합물 정보와 더불어 처리용량과 시간 세포주의 기저 유전자 발현 정보를 사용함으로써 정밀한 전사체 변화 예측을 가능하게 한다 따라서 본 모델이 생성한 전사체 변화 데이터를 활용함으로써 약물에 대한 이해도를 향상하고 신약 개발 및 정밀 의료 기술의 발전 등에 기여할 수 있는 가능성을 보여준다.},
keywords = {Artificial Intelligence, Bioinformatics, Drugs, Transcriptome},
pubstate = {published},
tppubtype = {conference}
}
본 논문에서는 Conditional Diffusion Model 기반 교란 조건을 고려한 전사체 변화 예측 심층 생성 모델을 소개한다 처리한 화합물 정보와 더불어 처리용량과 시간 세포주의 기저 유전자 발현 정보를 사용함으로써 정밀한 전사체 변화 예측을 가능하게 한다 따라서 본 모델이 생성한 전사체 변화 데이터를 활용함으로써 약물에 대한 이해도를 향상하고 신약 개발 및 정밀 의료 기술의 발전 등에 기여할 수 있는 가능성을 보여준다.
2024
3.
Yeabean Na; Junho Kim; Myung-Gyun Kang; Sunyong Yoo
Abstract | Links | BibTeX | Tags: Bioinformatics, Deep learning, Drugs
@conference{Yoo2024,
title = {A Multimodal Deep Learning Approach for Predicting Drug Metabolism According to the CYP2D6 Genetic Variation},
author = {Yeabean Na and Junho Kim and Myung-Gyun Kang and Sunyong Yoo},
url = {https://dtmbio.net/},
year = {2024},
date = {2024-01-02},
urldate = {2024-01-02},
publisher = {The 18th International Conference on Data and Text Mining in Biomedical Informatics},
abstract = {Background Cytochrome P450 2D6 (CYP2D6) is involved in metabolizing up to 25% of the drugs commonly used in clinics. Characterized by high polymorphisms, CYP2D6 is one of the key pharmacogenes in pharmacogenomics. This genetic variability can lead to significant inter-patient differences in drug metabolism, resulting in differential therapeutic responses and adverse effects. However, conducting in vivo or in vitro experiments for each CYP2D6 variant across various drugs is time-consuming, ethically challenging, and expensive. Given these constraints, In silico modeling approaches for predicting the drug metabolism profiles of CYP2D6 variants are a critical necessity.
Methods A multimodal deep learning approach that combined CYP2D6 genotype data and drug structural information was used in this study. A Convolutional Neural Network (CNN) was used to encode the genotype data, and a Graph Convolutional Network (GCN) was used to decode the drug structures. These diverse data types were then integrated into a multimodal model to predict drug metabolism.
Results A comparative analysis was conducted between a CNN model utilizing solely the CYP2D6 genotype data and a multimodal model incorporating both genotype and drug-specific information. The multimodal approach demonstrated better performance across all evaluated metrics. An additional experiment predicting drug metabolism on unseen drug data also performed well.
Conclusions This model is anticipated to enhance the prediction of metabolic capacity in previously uncharacterized CYP2D6 variants, potentially reducing adverse drug reactions.},
keywords = {Bioinformatics, Deep learning, Drugs},
pubstate = {published},
tppubtype = {conference}
}
Background Cytochrome P450 2D6 (CYP2D6) is involved in metabolizing up to 25% of the drugs commonly used in clinics. Characterized by high polymorphisms, CYP2D6 is one of the key pharmacogenes in pharmacogenomics. This genetic variability can lead to significant inter-patient differences in drug metabolism, resulting in differential therapeutic responses and adverse effects. However, conducting in vivo or in vitro experiments for each CYP2D6 variant across various drugs is time-consuming, ethically challenging, and expensive. Given these constraints, In silico modeling approaches for predicting the drug metabolism profiles of CYP2D6 variants are a critical necessity.
Methods A multimodal deep learning approach that combined CYP2D6 genotype data and drug structural information was used in this study. A Convolutional Neural Network (CNN) was used to encode the genotype data, and a Graph Convolutional Network (GCN) was used to decode the drug structures. These diverse data types were then integrated into a multimodal model to predict drug metabolism.
Results A comparative analysis was conducted between a CNN model utilizing solely the CYP2D6 genotype data and a multimodal model incorporating both genotype and drug-specific information. The multimodal approach demonstrated better performance across all evaluated metrics. An additional experiment predicting drug metabolism on unseen drug data also performed well.
Conclusions This model is anticipated to enhance the prediction of metabolic capacity in previously uncharacterized CYP2D6 variants, potentially reducing adverse drug reactions.
Methods A multimodal deep learning approach that combined CYP2D6 genotype data and drug structural information was used in this study. A Convolutional Neural Network (CNN) was used to encode the genotype data, and a Graph Convolutional Network (GCN) was used to decode the drug structures. These diverse data types were then integrated into a multimodal model to predict drug metabolism.
Results A comparative analysis was conducted between a CNN model utilizing solely the CYP2D6 genotype data and a multimodal model incorporating both genotype and drug-specific information. The multimodal approach demonstrated better performance across all evaluated metrics. An additional experiment predicting drug metabolism on unseen drug data also performed well.
Conclusions This model is anticipated to enhance the prediction of metabolic capacity in previously uncharacterized CYP2D6 variants, potentially reducing adverse drug reactions.
2022
2.
Myeonghyeon Jeong; Sangjin Kim; Yewon Han; Jihyun Jeong; Dahwa Jung; Inyoung Choi; Sunyong Yoo
BibTeX | Tags: Attention mechanism, Bioinformatics, Deep learning
@conference{nokey,
title = {Attention-based Deep Neural Network for Predicting Fetotoxicity},
author = {Myeonghyeon Jeong and Sangjin Kim and Yewon Han and Jihyun Jeong and Dahwa Jung and Inyoung Choi and Sunyong Yoo},
year = {2022},
date = {2022-01-02},
urldate = {2022-01-02},
booktitle = {In the 10th International Conference on Big Data Applications and Services},
publisher = {The Korea Big Data Service Society},
keywords = {Attention mechanism, Bioinformatics, Deep learning},
pubstate = {published},
tppubtype = {conference}
}
2015
1.
Moonshik Shin; Sungyoung Yoo; Suhyun Ha; Kyungrin Noh; Doheon Lee
Abstract | Links | BibTeX | Dimensions | Tags: ADME, Bioinformatics, Natural product
@conference{shin2015identifying,
title = {Identifying Potential Bioactive Compounds of Natural Products by Combining ADMET Prediction Methods},
author = {Moonshik Shin and Sungyoung Yoo and Suhyun Ha and Kyungrin Noh and Doheon Lee},
url = {https://dl.acm.org/doi/abs/10.1145/2811163.2811168},
doi = {10.1145/2811163.2811168},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {Proceedings of the ACM Ninth International Workshop on Data and Text Mining in Biomedical Informatics},
pages = {19–19},
publisher = {CIKM},
abstract = {Herbs consist of various chemical compounds. Thus, identifying potential bioactive compounds from those diversity is an important task for studies in the herb, food and natural products. Even though various computational approaches are developed for predicting bioactive compounds, the prediction performances are diverse due to different methods and dataset. Therefore, there is urgent demand for an approach that connotes the previous methods and identify potential bioactive compounds with high accuracy. To meet the demand, we proposed a filtering strategy that identifies potential bioactive compounds by combining previously developed computational methods which predict ADMET, such as Human Intestinal Absorption (HIA) and Caco-2 permeability. Our approach was evaluated on 930 compounds that are known as bioactive compounds, which were extracted from literature, DrugBank and Dr. Dukes phytochemical databases. By applying our filtering strategy, 97.5% of the known bioactive compounds were correctly predicted as bioactive. We examined whether our approach can distinguish the potential bioactive compound from the non-potential bioactive compounds with Fishers' exact test, and a reasonable p-value (3.806 x 10-9) was derived. For the next step, we are planning to develop a machine-learning based method to improve our filtering approach.},
keywords = {ADME, Bioinformatics, Natural product},
pubstate = {published},
tppubtype = {conference}
}
Herbs consist of various chemical compounds. Thus, identifying potential bioactive compounds from those diversity is an important task for studies in the herb, food and natural products. Even though various computational approaches are developed for predicting bioactive compounds, the prediction performances are diverse due to different methods and dataset. Therefore, there is urgent demand for an approach that connotes the previous methods and identify potential bioactive compounds with high accuracy. To meet the demand, we proposed a filtering strategy that identifies potential bioactive compounds by combining previously developed computational methods which predict ADMET, such as Human Intestinal Absorption (HIA) and Caco-2 permeability. Our approach was evaluated on 930 compounds that are known as bioactive compounds, which were extracted from literature, DrugBank and Dr. Dukes phytochemical databases. By applying our filtering strategy, 97.5% of the known bioactive compounds were correctly predicted as bioactive. We examined whether our approach can distinguish the potential bioactive compound from the non-potential bioactive compounds with Fishers' exact test, and a reasonable p-value (3.806 x 10-9) was derived. For the next step, we are planning to develop a machine-learning based method to improve our filtering approach.