@article{Yu2025,

title = {Multi-Level Association Rule Mining and Network Pharmacology to Identify the Polypharmacological Effects of Herbal Materials and Compounds from Traditional Medicine},

author = {Hyejin Yu and Kwanyong Choi and Ji Yeon Kim and Sunyong Yoo},

doi = {10.1093/bib/bbaf328},

year  = {2025},

date = {2025-07-01},

urldate = {2025-07-01},

journal = {Briefings in Bioinformatics},

note = {Correspondence to Sunyong Yoo},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Park2025,

title = {Characteristics of Children with Disability through Infant and Children’s Health Screening in South Korea},

author = {Junyong Park and Hwa-Jin Cho and Sunyong Yoo and Mim-Keun Song},

year  = {2025},

date = {2025-06-30},

urldate = {2025-06-30},

journal = {Annals of Medicine},

note = {Correspondence to Sunyong Yoo},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jung2024,

title = {Interpretable prediction of drug-drug interactions via text embedding in biomedical literature},

author = {Sunwoo Jung and Sunyong Yoo},

url = {https://www.sciencedirect.com/science/article/pii/S0010482524015816},

doi = {10.1016/j.compbiomed.2024.109496},

isbn = {0010-4825},

year  = {2025},

date = {2025-02-01},

urldate = {2025-02-01},

journal = {Computers in Biology and Medicine},

volume = {185},

pages = {109496},

abstract = {Polypharmacy is a promising approach for treating diseases, especially those with complex symptoms. However, it can lead to unexpected drug-drug interactions (DDIs), potentially reducing efficacy and triggering adverse drug reactions (ADRs). Predicting the risk of DDIs is crucial for ensuring safe drug use, particularly by identifying the types of DDIs and the mechanisms involved. Therefore, this study used biomedical literature to proposed hierarchical attention-based deep learning models to predict DDIs and their types. The proposed model consists of two components: drug embedding and DDI prediction. The drug embedding module extracts representation vectors that effectively capture drug properties using sentence and sequence embedding methods. For sentence embedding, a pre-trained biomedical language model is used to map drug-related sentences into vector space. For sequence embedding, sentence embedding vectors are sequentially fed into bidirectional long short-term memory with a hierarchical attention network, enabling the analysis of sentences relevant to DDI prediction while accounting for the order of the sentences. Finally, DDI prediction is performed using a deep neural network based on the sequence embedding vectors of a drug pair. Our model achieved high performances in the accuracy (0.85–0.90), AUROC (0.98–0.99), and AUPR (0.63–0.95) performance across 164 DDI types. Additionally, the proposed model showed improvements in up to 11 % in AUROC, and 8 % in AUPR. Furthermore, model interprets predictions by leveraging attention mechanisms and drug similarity. The results indicated that the model considered various factors beyond similarity to predict DDIs. These findings may help prevent unforeseen medical accidents and reduce healthcare costs by predicting detailed drug interaction types.},

note = {Correspondence to Sunyong Yoo},

keywords = {ADR, Artificial Intelligence, Attention mechanism, Bioinformatics, DDI, Deep learning, Text mining},

pubstate = {published},

tppubtype = {article}

}

@article{Lee2025,

title = {hERGAT: predicting hERG blockers using graph attention mechanism through atom- and molecule-level interaction analyses},

author = {Dohyeon Lee and Sunyong Yoo},

url = {https://link.springer.com/article/10.1186/s13321-025-00957-x?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20250128&utm_content=10.1186/s13321-025-00957-x},

doi = {10.1186/s13321-025-00957-x},

issn = {1758-2946},

year  = {2025},

date = {2025-01-28},

urldate = {2025-01-28},

journal = {Journal of Cheminformatics},

volume = {17},

number = {11},

abstract = {The human ether-a-go-go-related gene (hERG) channel plays a critical role in the electrical activity of the heart, and its blockers can cause serious cardiotoxic effects. Thus, screening for hERG channel blockers is a crucial step in the drug development process. Many in silico models have been developed to predict hERG blockers, which can efficiently save time and resources. However, previous methods have found it hard to achieve high performance and to interpret the predictive results. To overcome these challenges, we have proposed hERGAT, a graph neural network model with an attention mechanism, to consider compound interactions on atomic and molecular levels. In the atom-level interaction analysis, we applied a graph attention mechanism (GAT) that integrates information from neighboring nodes and their extended connections. The hERGAT employs a gated recurrent unit (GRU) with the GAT to learn information between more distant atoms. To confirm this, we performed clustering analysis and visualized a correlation heatmap, verifying the interactions between distant atoms were considered during the training process. In the molecule-level interaction analysis, the attention mechanism enables the target node to focus on the most relevant information, highlighting the molecular substructures that play crucial roles in predicting hERG blockers. Through a literature review, we confirmed that highlighted substructures have a significant role in determining the chemical and biological characteristics related to hERG activity. Furthermore, we integrated physicochemical properties into our hERGAT model to improve the performance. Our model achieved an area under the receiver operating characteristic of 0.907 and an area under the precision-recall of 0.904, demonstrating its effectiveness in modeling hERG activity and offering a reliable framework for optimizing drug safety in early development stages.},

note = {Correspondence to Sunyong Yoo},

keywords = {Artificial Intelligence, Attention mechanism, Bioinformatics, Cardiotoxicity, Deep learning, Graph attention network},

pubstate = {published},

tppubtype = {article}

}

@article{박준영;유선용2025,

title = {화합물의 골격구조를 활용한 Transformer 기반 새로운 분자 설계},

author = {박준영 and 유선용},

url = {http://journal.dcs.or.kr/_common/do.php?a=full&b=12&bidx=3950&aidx=43776},

doi = {10.9728/dcs.2025.26.1.217},

issn = {1598-2009},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {디지털콘텐츠학회논문지},

volume = {26},

number = {1},

pages = {217-223},

abstract = {전통적인 신약 개발은 새로운 약물을 시장에 출시하기까지 많은 시간과 막대한 비용이 소요되며, 높은 실패율로 인해 효율성이 낮다는 문제가 있다. 이러한 문제를 해결하기 위해 생성 모델을 활용한 혁신적인 접근법이 주목받고 있다. 본 연구에서는 트랜스포머 디코더 구조를 기반으로 화합물의 구조 정보를 문자열로 학습하여 새로운 화합물 구조를 생성하는 모델을 제안한다. 특히, 화합물에서 추출한 골격 구조(scaffold)를 임베딩하여 모델 입력에 포함함으로써, 결합 및 원자 정보와 골격 구조를 동시에 처리하였다. 벤치마크 데이터셋을 사용한 평가 결과, 골격 구조 임베딩을 적용한 모델이 데이터셋 별로 유효성 지표에서 0.964, 0.986의 우수한 성능을 보였다. 본 연구는 분자 생성 모델에 골격 구조 임베딩을 도입함으로써, 화학적 규칙을 준수하는 분자를 효과적으로 생성할 수 있는 방법을 제시하였으며, 신약 개발 분야에서 AI 기반 분자 설계의 효율성을 높이는 데 기여할 것으로 기대된다.},

keywords = {Bioinformatics, Drugs, Transformer},

pubstate = {published},

tppubtype = {article}

}

@article{nokeye,

title = {Prediction of bioactive compounds hepatotoxicity using in silico and in vitro analysis},

author = {Kwanyong Choi and Soyeon Lee and Sunyong Yoo and Hyoung-Yun Han and Soo-yeon Park and Ji Yeon Kim},

doi = {10.1186/s13765-024-00961-z},

year  = {2024},

date = {2024-12-17},

urldate = {2024-12-17},

journal = {Applied Biological Chemistry},

volume = {67},

number = {107},

abstract = {The leading safety issue and side effect associated with natural herb products is drug-induced liver injury (DILI) caused by bioactive compounds derived from the herb products. Herein, in silico and in vitro analyses were compared to determine the hepatotoxicity of compounds. The results of in silico analyses, which included an integrated database and an interpretable DILI prediction model, identified calycosin, biochanin_A, xanthatin, piperine, and atractyloside as potential hepatotoxic compounds and tenuifolin as a non-hepatotoxic compound. To evaluate the viability of HepG2 cells exposed to the selected compounds, we determined the IC50 and IC20 values of viability using MTT assays. For in-depth screening, we performed hematoxylin and eosin-stained morphological screens, JC-1 mitochondrial assays, and mRNA microarrays. The results indicated that calycosin, biochanin_A, xanthatin, piperine, and atractyloside were potential hepatotoxicants that caused decreased viability and an apoptotic phase in morphology, while these effects were not observed for tenuifolin, a non-hepatotoxicant. In the JC-1 assay, apoptosis was induced by all the predicted hepatotoxicants except atractyloside. According to transcriptomic analysis, all the compounds predicted to induce DILI showed hepatotoxic effects. These results highlighted the importance of using in vitro assays to validate predictive in silico models and determine the potential of bioactive compounds to induce hepatotoxicity in humans.},

note = {Correspondence to Ji Yeon Kim},

keywords = {Drug-induced liver injury, in silico, in vitro},

pubstate = {published},

tppubtype = {article}

}

@article{lee2024enhancing,

title = {Enhancing intestinal health with germinated oats: Bioinformatics and compound profiling insights into a novel approach for managing inflammatory bowel disease},

author = {Hyeon Jae Lee and Kyeong Jin Kim and Soo-yeon Park and Kwanyong Choi and Jaeho Pyee and Sunyong Yoo and Ji Yeon Kim},

url = {https://www.sciencedirect.com/science/article/pii/S221242922401263X},

doi = {10.1016/j.fbio.2024.104833},

year  = {2024},

date = {2024-10-01},

urldate = {2024-10-01},

journal = {Food Bioscience},

volume = {61},

pages = {104833},

publisher = {Elsevier},

abstract = {Oats are widely recognized for their numerous health benefits, particularly regarding their anti-inflammatory properties. However, research exploring their specific effects on intestinal permeability and tight junction (TJ) integrity in the context of inflammatory bowel disease (IBD) has been limited. This study aimed to investigate the therapeutic efficacy of germinated oat extract (GOE) in managing IBD, a condition marked by persistent gastrointestinal inflammation and increasing global prevalence. The identified compounds were used to predict target biomarkers and mechanisms related to IBD via bioinformatics analysis and validated using in vitro models. In this study, we used network biology and chemical informatics approaches to predict target biomarkers and their molecular mechanisms. The predicted biomarkers were validated for their effectiveness using a cellular model of intestinal inflammation. The effectiveness of treatment with GOE was validated via in vitro studies, which demonstrated significant enhancement in transepithelial electrical resistance (TEER) and a reduction in fluorescein isothiocyanate (FITC) permeability. Analysis of the mRNA expression of IBD-associated biomarkers in Caco-2 cells demonstrated a significant increase in the mRNA levels of TJ proteins, including TJP1, TJP2, occludin, claudin-1 and claudin-3 compared to the inflammatory group. Furthermore, treatment with GOE markedly reduced the mRNA expression levels of proinflammatory cytokines such as TNF-α, IL-6, and CXCL8. The combination of COCONUT and chemical profiling analysis provided insights into the fundamental molecular mechanisms of GOE. These results underscore the potential of systematically using big data-driven network biology to analyze the effect of food components, highlighting GOE as a promising dietary intervention for IBD.},

note = {Correspondence to Ji Yeon Kim},

keywords = {Bioinformatics, Gut permeability, Inflammatory bowel disease, Network analysis},

pubstate = {published},

tppubtype = {article}

}

@article{kim2024passing,

title = {Passing Word Line-Induced Subthreshold Leakage Reduction Using a Partial Insulator in a Buried Channel Array Transistor},

author = {Suyeon Kim and Dong Young Kim and Je Won Park and Shinwook Kim and Seungchan Lee and Han Seung Jang and Jinseok Park and Sunyong Yoo and Myoung Jin Lee},

url = {https://ieeexplore.ieee.org/abstract/document/10495758},

doi = {10.1109/TED.2024.3379963},

issn = {0018-9383},

year  = {2024},

date = {2024-04-10},

urldate = {2024-04-10},

journal = {IEEE Transactions on Electron Devices},

volume = {71},

issue = {5},

pages = {2976 - 2982},

publisher = {IEEE},

abstract = {As dynamic random access memory (DRAM) technologies continue to be downscaled, the partial isolation type buried channel array transistor (Pi-BCAT) structure has emerged as an innovative solution for the increasing challenges caused by leakage current adjacent to passing word lines (PWLs). This study reveals that the Pi-BCAT reduces leakage currents by 30% when compared to conventional BCAT structures. Our comprehensive simulations demonstrate that Pi-BCAT is resistant to temperature-induced leakage variations, confirming its significance in promoting consistent device performance and power management. The Pi-BCAT structure is predicted to be crucial in the advancement of DRAM reliability and efficiency, hence initiating further advancements in semiconductor technology.},

note = {Correspondence to Sunyong Yoo and Myoung Jin Lee},

keywords = {Optimization},

pubstate = {published},

tppubtype = {article}

}

@article{jeong2024fetoml,

title = {FetoML: Interpretable predictions of the fetotoxicity of drugs based on machine learning approaches},

author = {Myeonghyeon Jeong and Sunyong Yoo},

url = {https://onlinelibrary.wiley.com/doi/full/10.1002/minf.202300312},

doi = {10.1002/minf.202300312},

issn = {1868-1743},

year  = {2024},

date = {2024-03-03},

urldate = {2024-03-03},

journal = {Molecular Informatics},

volume = {43},

number = {6},

pages = {e202300312},

publisher = {Wiley Online Library},

abstract = {Pregnant females may use medications to manage health problems that develop during pregnancy or that they had prior to pregnancy. However, using medications during pregnancy has a potential risk to the fetus. Assessing the fetotoxicity of drugs is essential to ensure safe treatments, but the current process is challenged by ethical issues, time, and cost. Therefore, the need for in silico models to efficiently assess the fetotoxicity of drugs has recently emerged. Previous studies have proposed successful machine learning models for fetotoxicity prediction and even suggest molecular substructures that are possibly associated with fetotoxicity risks or protective effects. However, the interpretation of the decisions of the models on fetotoxicity prediction for each drug is still insufficient. This study constructed machine learning-based models that can predict the fetotoxicity of drugs while providing explanations for the decisions. For this, permutation feature importance was used to identify the general features that the model made significant in predicting the fetotoxicity of drugs. In addition, features associated with fetotoxicity for each drug were analyzed using the attention mechanism. The predictive performance of all the constructed models was significantly high (AUROC: 0.854-0.974, AUPR: 0.890-0.975). Furthermore, we conducted literature reviews on the predicted important features and found that they were highly associated with fetotoxicity. We expect that our model will benefit fetotoxicity research by providing an evaluation of fetotoxicity risks for drugs or drug candidates, along with an interpretation of that prediction.},

note = {Correspondence to Sunyong Yoo},

keywords = {Attention mechanism, Bioinformatics, Deep learning, Fetotoxicity, in silico, Interpretability},

pubstate = {published},

tppubtype = {article}

}

@article{yoo2024interpretable,

title = {Interpretable Prediction of SARS-CoV-2 Epitope-specific TCR Recognition Using a Pre-Trained Protein Language Model},

author = {Sunyong Yoo and Myeonghyeon Jeong and Subhin Seomun and Kiseong Kim and Youngmahn Han},

url = {https://ieeexplore.ieee.org/abstract/document/10443062},

doi = {10.1109/TCBB.2024.3368046},

year  = {2024},

date = {2024-02-21},

urldate = {2024-02-21},

journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},

volume = {21},

issue = {3},

pages = {428-438},

publisher = {IEEE},

abstract = {The emergence of the novel coronavirus, designated as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has posed a significant threat to public health worldwide. There has been progress in reducing hospitalizations and deaths due to SARS-CoV-2. However, challenges stem from the emergence of SARS-CoV-2 variants, which exhibit high transmission rates, increased disease severity, and the ability to evade humoral immunity. Epitope-specific T-cell receptor (TCR) recognition is key in determining the T-cell immunogenicity for SARS-CoV-2 epitopes. Although several data-driven methods for predicting epitope-specific TCR recognition have been proposed, they remain challenging due to the enormous diversity of TCRs and the lack of available training data. Self-supervised transfer learning has recently been proven useful for extracting information from unlabeled protein sequences, increasing the predictive performance of fine-tuned models, and using a relatively small amount of training data. This study presents a deep-learning model generated by fine-tuning pre-trained protein embeddings from a large corpus of protein sequences. The fine-tuned model showed markedly high predictive performance and outperformed the recent Gaussian process-based prediction model. The output attentions captured by the deep-learning model suggested critical amino acid positions in the SARS-CoV-2 epitope-specific TCRβ sequences that are highly associated with the viral escape of T-cell immune response.},

note = {Correspondence to Sunyong Yoo},

keywords = {Amino acids, Attention mechanism, Bioinformatics, Coronaviruses, Deep learning, Immune system, Lymphocytes, Predictive models, Proteins, Transformer},

pubstate = {published},

tppubtype = {article}

}

@article{lee2024interdili,

title = {InterDILI: interpretable prediction of drug-induced liver injury through permutation feature importance and attention mechanism},

author = {Soyeon Lee and Sunyong Yoo},

url = {https://link.springer.com/article/10.1186/s13321-023-00796-8},

doi = {10.1186/s13321-023-00796-8},

year  = {2024},

date = {2024-01-03},

urldate = {2024-01-03},

journal = {Journal of Cheminformatics},

volume = {16},

number = {1},

pages = {1},

publisher = {Springer},

abstract = {Safety is one of the important factors constraining the distribution of clinical drugs on the market. Drug-induced liver injury (DILI) is the leading cause of safety problems produced by drug side effects. Therefore, the DILI risk of approved drugs and potential drug candidates should be assessed. Currently, in vivo and in vitro methods are used to test DILI risk, but both methods are labor-intensive, time-consuming, and expensive. To overcome these problems, many in silico methods for DILI prediction have been suggested. Previous studies have shown that DILI prediction models can be utilized as prescreening tools, and they achieved a good performance. However, there are still limitations in interpreting the prediction results. Therefore, this study focused on interpreting the model prediction to analyze which features could potentially cause DILI. For this, five publicly available datasets were collected to train and test the model. Then, various machine learning methods were applied using substructure and physicochemical descriptors as inputs and the DILI label as the output. The interpretation of feature importance was analyzed by recognizing the following general-to-specific patterns: (i) identifying general important features of the overall DILI predictions, and (ii) highlighting specific molecular substructures which were highly related to the DILI prediction for each compound. The results indicated that the model not only captured the previously known properties to be related to DILI but also proposed a new DILI potential substructural of physicochemical properties. The models for the DILI prediction achieved an area under the receiver operating characteristic (AUROC) of 0.88–0.97 and an area under the Precision-Recall curve (AUPRC) of 0.81–0.95. From this, we hope the proposed models can help identify the potential DILI risk of drug candidates at an early stage and offer valuable insights for drug development.},

note = {Correspondence to Sunyong Yoo},

keywords = {Artificial Intelligence, Attention mechanism, Bioinformatics, Deep learning, Drug-induced liver injury, Feature importance, Hepatotoxicity, in silico},

pubstate = {published},

tppubtype = {article}

}

@article{정선우2024drug,

title = {Drug-Drug Interaction Prediction Model Based on Deep Learning Using Drug Information Document Embedding},

author = {정선우 and 유선용},

url = {https://www.dbpia.co.kr/pdf/pdfView.do?nodeId=NODE11852157&googleIPSandBox=false&mark=0&minRead=10&ipRange=false&b2cLoginYN=false&icstClss=010000&isPDFSizeAllowed=true&nodeHistoryTotalCnt=2&accessgl=Y&language=ko_KR&hasTopBanner=true},

doi = {10.5626/JOK.2024.51.6.503},

issn = {2833-6296},

year  = {2024},

date = {2024-01-02},

urldate = {2024-01-02},

journal = {Journal of KIISE},

volume = {51},

number = {6},

pages = {503–512},

abstract = {다약제는 암, 고혈압, 천식 등 다양한 질병에 대하여 유망한 접근법이다. 일반적으로 병원에 방문하는 환자는 2종 이상의 약물을 처방받는다. 그러나 다약제의 사용은 개별 약물이 목표하는 작용 외에 예상치 못한 상호작용을 유발할 수 있다. 약물 간 상호작용을 사전에 예측하는 것은 안전한 약물 사용을 위한 매우 중요한 과제이다. 본 연구에서는 다약제 사용 시 발생 가능한 약물 간 상호작용 예측을 위해 개별 약물 정보를 포함한 문서를 이용하여 약물을 표현하는 문서 임베딩 기반의 딥러닝 예측 모델을 제안한다. 약물 정보 문서는 DrugBank 데이터를 이용해 약물의 설명, 적응증, 약력학 정보, 작용 기전, 독성 속성을 결합해 구축한다. 그 후 Doc2Vec, BioSentVec 언어 모델을 통해 약물 문서로부터 약물 표현 벡터를 생성한다. 두 약물 표현 벡터는 한 쌍으로 묶여 딥러닝 기반 예측 모델에 입력되고, 해당 모델은 두 약물 간 상호작용을 예측한다. 본 논문에서는 언어 임베딩 모델의 성능 비교, 데이터의 불균형도 조절 등 다양한 조건의 변화에 따른 실험 결과의 차이를 분석하여 약물 간 상호작용 예측을 위한 최적의 모델을 구축하는 것을 목표로 한다. 제안된 모델은 약물 처방 과정, 신약 개발의 임상 과정 등에서 약물간 상호작용 사전 예측을 위하여 활용될 수 있을 것으로 기대된다.},

note = {Correspondence to Sunyong Yoo},

keywords = {ADR, DDI, Deep learning, Text mining},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {메시지 패싱 그래프 기반 딥러닝 모델을 활용한 화합물의 심장독성 예측},

author = {이도현 and 유선용},

url = {https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11956044},

doi = {10.9728/dcs.2024.25.10.2961},

isbn = {1598-2009},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {한국디지털콘텐츠학회},

volume = {25},

number = {10},

pages = {2961-2968},

abstract = {hERG 채널은 심장의 전기 활동에 필수적이며, 이 채널을 차단하는 물질은 심각한 심장 독성 효과를 일으킬 수 있다. 인실리코 예측 모델은 hERG 차단제를 효율적으로 선별할 수 있어 시간과 자원을 절약할 수 있다. 이전 접근법은 예측 결과를 해석하고 분자 구조-기능 관계를 이해하는 데 어렵다. 본 연구에서는 공개 데이터베이스로부터 화합물을 수집하여 12,920개의 데이터셋을 구축 하였다. 화합물의 그래프 구조를 고려하는 그래프 신경망(GNN) 가운데 메시지 패싱 신경망(MPNN)을 활용하여 특징 벡터를 추출하고, 이를 구조적ㆍ물리화학적 특성과 결합하여 최종 hERG 차단제를 예측하였다. 해당 모델은 AUROC는 0.864 (±0.009), AUPR은 0.907 (±0.010)의 성능을 달성하였다. 실험 결과, 제안된 모델은 그래프 특징 벡터를 통합하여 분자 특성을 효과적으로 반영하고 분자 간의 관계를 예측하여 hERG 차단제를 예측할 수 있음을 시사한다. 본 연구는 약물 개발과정에서 예비 도구로 활용되어 심장독성을 조기에 평가할 수 있을 것이다.},

note = {Correspondence to Sunyong Yoo},

keywords = {Bioinformatics, Cardiotoxicity, Graph attention network},

pubstate = {published},

tppubtype = {article}

}

@article{yoo2023medical,

title = {Medical service utilization by children with physical or brain disabilities in South Korea},

author = {Sunyong Yoo and Ja Young Choi and Shin-seung Yang and Seong-Eun Koh and Myeong-Hyeon Jeong and Min-Keun Song},

url = {https://link.springer.com/article/10.1186/s12887-023-04309-2},

doi = {10.1186/s12887-023-04309-2},

year  = {2023},

date = {2023-09-26},

urldate = {2023-09-26},

journal = {BMC pediatrics},

volume = {23},

number = {1},

pages = {487},

publisher = {Springer},

abstract = {Background 

Children with physical or brain disabilities experience several functional impairments and declining health complications that must be considered for adequate medical support. This study investigated the current medical service utilization of children expressing physical or brain disabilities in South Korea by analyzing medical visits, expenses, and comorbidities. 

Methods 

We used a database linked to the National Rehabilitation Center of South Korea to extract information on medical services utilized by children with physical or brain disabilities, the number of children with a disability, medical visits for each child, medical expenses per visit, total medical treatment cost, copayments by age group, condition severity, and disability type. 

Results 

Brain disorder comorbidities significantly differed between those with mild and severe disabilities. Visits per child, total medical treatment cost, and copayments were higher in children with severe physical disabilities; however, medical expenses per visit were lower than those with mild disabilities. These parameters were higher in children with severe brain disabilities than in mild cases. Total medical expenses incurred by newborns to three-year-old children with physical disorders were highest due to increased visits per child. However, medical expenses per visit were highest for children aged 13–18. 

Conclusion 

Medical service utilization varied by age, condition severity, and disability type. Severe cases and older children with potentially fatal comorbidities required additional economic support. Therefore, a healthcare delivery system for children with disabilities should be established to set affordable medical costs and provide comprehensive medical services based on disability type and severity.},

note = {Correspondence to Min-Keun Song},

keywords = {Medical informatics, National health insurance service},

pubstate = {published},

tppubtype = {article}

}

@article{jung2023identification,

title = {Identification of Breast Cancer Metastasis Markers from Gene Expression Profiles Using Machine Learning Approaches},

author = {Jinmyung Jung and Sunyong Yoo},

url = {https://www.mdpi.com/2073-4425/14/9/1820},

doi = {10.3390/genes14091820},

year  = {2023},

date = {2023-09-20},

urldate = {2023-09-20},

journal = {Genes},

volume = {14},

number = {9},

pages = {1820},

publisher = {MDPI},

abstract = {Cancer metastasis accounts for approximately 90% of cancer deaths, and elucidating markers in metastasis is the first step in its prevention. To characterize metastasis marker genes (MGs) of breast cancer, XGBoost models that classify metastasis status were trained with gene expression profiles from TCGA. Then, a metastasis score (MS) was assigned to each gene by calculating the inner product between the feature importance and the AUC performance of the models. As a result, 54, 202, and 357 genes with the highest MS were characterized as MGs by empirical p-value cutoffs of 0.001, 0.005, and 0.01, respectively. The three sets of MGs were compared with those from existing metastasis marker databases, which provided significant results in most comparisons (p-value < 0.05). They were also significantly enriched in biological processes associated with breast cancer metastasis. The three MGs, SPPL2C, KRT23, and RGS7, showed highly significant results (p-value < 0.01) in the survival analysis. The MGs that could not be identified by statistical analysis (e.g., GOLM1, ELAVL1, UBP1, and AZGP1), as well as the MGs with the highest MS (e.g., ZNF676, FAM163B, LDOC2, IRF1, and STK40), were verified via the literature. Additionally, we checked how close the MGs were to each other in the protein–protein interaction networks. We expect that the characterized markers will help understand and prevent breast cancer metastasis.},

note = {Correspondence to Sunyong Yoo},

keywords = {Bioinformatics, Breast cancer, Feature importance, Gene expression, Machine learning, Metastasis marker},

pubstate = {published},

tppubtype = {article}

}

@article{이소연2023기계학습을,

title = {기계학습을 활용한 화합물의 약인성 간 손상 예측 방법 연구},

author = {이소연 and 유선용},

url = {https://www.dbpia.co.kr/pdf/pdfView.do?nodeId=NODE11519759&googleIPSandBox=false&mark=0&minRead=10&ipRange=false&b2cLoginYN=false&icstClss=010000&isPDFSizeAllowed=true&nodeHistoryTotalCnt=2&accessgl=Y&language=ko_KR&hasTopBanner=true},

doi = {10.5626/JOK.2023.50.9.777},

issn = {2383-6296},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {정보과학회논문지},

volume = {50},

number = {9},

pages = {777–783},

abstract = {약 약인성 간 손상은 임상시험용 의약품이 시장에 유통되는 것을 막는 요인 중 하나이다.  따라서 사전에 화합물의 약인성 간 손상 위험 평가가 필요하다.  안전성을 평가하기 위해 생체 내 (in  vivo)  및 시험관 내 시험 방법(in  vitro)이 사용되지만 이들은 시간과 비용이 많이 든다.  본 연구에서는 위의 문제를 극복하고자 random  forest,  light  gradient  boosting  machine,  logistic  regression  모델을 제안한다.  모델은 입력으로 화합물의 분자 구조와 물리화학적 특징을 사용하고 출력으로 약인성 간 손상을 예측한다.  최적의 모델은 평가 지표에서 전반적으로 좋은 성능을 보인 random  forest였다.  본 연구에서 제안된 모델은 신약 후보물질의 잠재적인 간 손상을 미리 파악함으로써 신약 개발 과정에 도움을 줄 수 있을 것으로 기대된다.},

note = {Correspondence to Sunyong Yoo},

keywords = {Hepatotoxicity, Machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{jeong2023predicting,

title = {Predicting the Fetotoxicity of Drugs Using Machine Learning},

author = {Myeonghyeon Jeong and Sunyong Yoo},

url = {https://koreascience.kr/article/JAKO202320150261638.page},

doi = {10.5352/JLS.2023.33.6.490},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Journal of Life Science},

volume = {33},

number = {6},

pages = {490–497},

publisher = {Korean Society of Life Science},

abstract = {Pregnant women may need to take medications to treat preexisting diseases or diseases that develop during pregnancy. However, some drugs may be fetotoxic and lead to, for example, teratogenicity and growth retardation. Predicting the fetotoxicity of drugs is thus important for the health of the mother and fetus. The fetotoxicity of many drugs has not been established because various challenges hinder the ability of researchers to determine their fetotoxicity. The need exists for in silico-based fetotoxicity assessment models, as they can modernize the testing paradigm, improve predictability, and reduce the use of animals and the costs of fetotoxicity testing. In this study, we collected data on the fetotoxicity of drugs and constructed fetotoxicity prediction models based on various machine learning algorithms. We optimized the models for more precise predictions by tuning the hyperparameters. We then performed quantitative performance evaluations. The results indicated that the constructed machine learning-based models had high performance (AUROC >0.85, AUPR >0.9) in fetotoxicity prediction. We also analyzed the feature importance of our model's predictions, which could be leveraged to identify the specific features of drugs that are strongly associated with fetotoxicity. The proposed model can be used to prescreen drugs and drug candidates at a lower cost and in less time. It provides a predictive score for fetotoxicity risk, which may be beneficial in the design of studies on fetotoxicity in human pregnancy.},

note = {Correspondence to Sunyong Yoo},

keywords = {Machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{lee2022relationship,

title = {The relationship between nutrient intake and cataracts in the older adult population of Korea},

author = {Sangyun Lee and Soyeon Lee and Myeonghyeon Jeong and Sunwoo Jung and Myoungjin Lee and Sunyong Yoo},

url = {https://www.mdpi.com/2072-6643/14/23/4962},

doi = {10.3390/nu14234962},

year  = {2022},

date = {2022-11-23},

urldate = {2022-11-23},

journal = {Nutrients},

volume = {14},

number = {23},

pages = {4962},

publisher = {MDPI},

abstract = {Cataracts are a prevalent ophthalmic disease worldwide, and research on the risk factors for cataracts occurrence is actively being conducted. This study aimed to investigate the relationship between nutrient intake and cataracts in the older adult population in Korea. We analyzed data from Korean adults over the age of 60 years (cataract: 2137, non-cataract: 3497) using the Korean National Health and Nutrition Examination Survey. We performed univariate simple and multiple logistic regressions, adjusting for socio-demographic, medical history, and lifestyle, to identify the associations between nutrient intake and cataracts. A higher intake of vitamin B1 in the male group was associated with a lower incidence of cataracts. A lower intake of polyunsaturated fatty acids and vitamin A, and a higher intake of vitamin B2 in the female group were associated with a higher incidence of cataracts. Our study demonstrated that polyunsaturated fatty acids, vitamin A, and vitamin B2 could affect the incidence of cataracts according to sex. The findings could be used to control nutrient intake for cataract prevention.},

note = {Correspondence to Sunyong Yoo},

keywords = {Cataracts, Medical informatics, NHANES, Nutrients, Nutrition surveys},

pubstate = {published},

tppubtype = {article}

}

@article{park2022row,

title = {Row hammer reduction using a buried insulator in a buried channel array transistor},

author = {Jin Hyo Park and Su Yeon Kim and Dong Young Kim and Geon Kim and Je Won Park and Sunyong Yoo and Young-Woo Lee and Myoung Jin Lee},

url = {https://ieeexplore.ieee.org/abstract/document/9938404},

doi = {10.1109/TED.2022.3215931},

year  = {2022},

date = {2022-11-03},

urldate = {2022-11-03},

journal = {IEEE Transactions on Electron Devices},

volume = {69},

number = {12},

pages = {6710–6716},

publisher = {IEEE},

abstract = {In this article, we propose an analysis of the usage of a partial isolation type buried channel array transistor (Pi-BCAT). Compared with other structures, the conventional BCAT exhibits improved characteristics in the row hammer effect (RHE) because of its shallow drain/body (D/B) junction. Nevertheless, it remains affected by the RHE and should be mitigated because it is directly related to the reliability of dynamic random access memory (DRAM) applications. The proposed device exhibits a 50% improvement in the RHE and reduces leakage current ( IOFF ) to one-third the level of conventional BCATs while also minimizing the ON -current ( ION ) reduction. Moreover, to efficiently compare RHE, we compare ΔVSN by RHE and ΔVSN based on the gate-induced drain leakage (GIDL) according to bias conditions and the device’s parameters. Finally, we optimize the parameter values of the buried insulator by considering electrical characteristics and the RHE.},

note = {Correspondence to Myoung Jin Lee},

keywords = {Optimization},

pubstate = {published},

tppubtype = {article}

}

@article{jung2022predicting,

title = {Predicting ischemic stroke in patients with atrial fibrillation using machine learning},

author = {Seonwoo Jung and Min-Keun Song and Eunjoo Lee and Sejin Bae and Yeon-Yong Kim and Doheon Lee and Myoung Jin Lee and Sunyong Yoo},

url = {https://www.imrpress.com/journal/FBL/27/3/10.31083/j.fbl2703080/htm?utm_source=TrendMD&utm_medium=cpc&utm_campaign=Frontiers_in_Bioscience-Landmark_TrendMD_1},

doi = {10.31083/j.fbl2703080},

year  = {2022},

date = {2022-03-04},

urldate = {2022-03-04},

journal = {Frontiers in Bioscience-Landmark},

volume = {27},

number = {3},

pages = {80},

publisher = {IMR Press},

abstract = {Background 

Atrial fibrillation (AF) is a well-known risk factor for stroke. Predicting the risk is important to prevent the first and secondary attacks of cerebrovascular diseases by determining early treatment. This study aimed to predict the ischemic stroke in AF patients based on the massive and complex Korean National Health Insurance (KNHIS) data through a machine learning approach. 

Methods 

We extracted 65-dimensional features, including demographics, health examination, and medical history information, of 754,949 patients with AF from KNHIS. Logistic regression was used to determine whether the extracted features had a statistically significant association with ischemic stroke occurrence. Then, we constructed the ischemic stroke prediction model using an attention-based deep neural network. The extracted features were used as input, and the occurrence of ischemic stroke after the diagnosis of AF was the output used to train the model. 

Results We found 48 features significantly associated with ischemic stroke occurrence through regression analysis (p-value < 0.001). When the proposed deep learning model was applied to 150,989 AF patients, it was confirmed that the occurrence ischemic stroke was predicted to be higher AUROC (AUROC = 0.727 ± 0.003) compared to CHA2DS2-VASc score (AUROC = 0.651 ± 0.007) and other machine learning methods. 

Conclusions 

As part of preventive medicine, this study could help AF patients prepare for ischemic stroke prevention based on predicted stoke associated features and risk scores.},

note = {Correspondence to Sunyong Yoo},

keywords = {Atrial fibrillation, Attention mechanism, Deep learning, Machine learning, Medical informatics, National health insurance service, Stroke},

pubstate = {published},

tppubtype = {article}

}

@article{park2021s,

title = {S-TAT leakage current in partial isolation type saddle-FinFET (Pi-FinFET) s},

author = {Jin Hyo Park and Geon Kim and Dong Yeong Kim and Su Yeon Kim and Sunyong Yoo and Myoung Jin Lee},

url = {https://ieeexplore.ieee.org/abstract/document/9507492},

doi = {10.1109/ACCESS.2021.3102687},

year  = {2021},

date = {2021-08-05},

urldate = {2021-08-05},

journal = {IEEE Access},

volume = {9},

pages = {111567–111575},

publisher = {IEEE},

abstract = {In this paper, we compare conventional saddle type FinFETs to partial isolation type saddle FinFETs (Pi-FinFETs) using 3D TCAD simulations to examine the effect of single charge traps for proper prediction of leakage current. We simulated single charge traps at various locations in the drain region, and analyzed how the traps affect leakage current. Our results show that Pi-FinFETs enhanced the leakage current characteristics given the presence of a single charge trap. Also, it was found that Pi-FinFETs exhibit half the FTAT of S-FinFETs. Based on the results from our analysis method, where we use Ioff fluctuation, the FTAT , the σF and the PF parameters to accurately compare performance, and present device design guidelines aimed at improving DRAM refresh characteristics.},

keywords = {Optimization},

pubstate = {published},

tppubtype = {article}

}

@article{masood2020optimalc,

title = {Optimal power allocation for maximizing energy efficiency in DAS-based IoT network},

author = {Zaki Masood and Hosung Park and Han Seung Jang and Sunyong Yoo and Sokhee P Jung and Yonghoon Choi},

url = {https://ieeexplore.ieee.org/abstract/document/9166712},

doi = {10.1109/JSYST.2020.3013693},

year  = {2021},

date = {2021-06-01},

urldate = {2021-06-01},

journal = {IEEE Systems Journal},

volume = {15},

number = {2},

pages = {2342–2348},

publisher = {IEEE},

abstract = {Distributed antenna system based on simultaneous wireless information and power transfer (SWIPT) can be one of the promising solutions in maximizing energy efficiency (EE), where ultra low power devices harvest energy in power splitting (PS) mode. The paradigm shift of the internet-of-things (IoT) has increased the number of IoT devices and associated sensitive data exchange on the internet. Like the EE is a noteworthy aspect in ultra low power devices, energy harvesting (EH) is an active approach from surrounding electromagnetic sources. This article deals with EE maximization for SWIPT using PS mode. In the SWIPT system, this article presents a tradeoff between EE and spectral efficiency and proposes an algorithm, which allocates optimal power to each distributed antenna port. For an IoT device, the PS scheme implements EH and information decoding operations. The proposed algorithm is based on the Lagrangian multiplier method and Karush-Kuhn-Tucker conditions to find the optimal solution without iterative computation compared to the conventional iterative method. Simulation results reveal that the proposed algorithm achieves maximum energy transfer by the using optimal PS ratio.},

keywords = {Optimization},

pubstate = {published},

tppubtype = {article}

}