PUBLICATIONS
2025
3.
Sunwoo Jung; Sunyong Yoo
Abstract | Links | BibTeX | Dimensions | Tags: ADR, Artificial Intelligence, Attention mechanism, Bioinformatics, DDI, Deep learning, Text mining
@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}
}
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.
2024
2.
정선우; 유선용
Abstract | Links | BibTeX | Dimensions | Tags: ADR, DDI, Deep learning, Text mining
@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}
}
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.
2018
1.
Sunyong Yoo; Kyungrin Noh; Moonshik Shin; Junseok Park; Kwang-Hyung Lee; Hojung Nam; Doheon Lee
Abstract | Links | BibTeX | Dimensions | Tags: ADR, Bioinformatics, Drugs, Network analysis
@article{yoo2018silico,
title = {In silico profiling of systemic effects of drugs to predict unexpected interactions},
author = {Sunyong Yoo and Kyungrin Noh and Moonshik Shin and Junseok Park and Kwang-Hyung Lee and Hojung Nam and Doheon Lee},
url = {https://www.nature.com/articles/s41598-018-19614-5},
doi = {10.1038/s41598-018-19614-5},
year = {2018},
date = {2018-01-25},
urldate = {2018-01-25},
journal = {Scientific Reports},
volume = {8},
number = {1},
pages = {1612},
publisher = {Nature Publishing Group UK London},
abstract = {Identifying unexpected drug interactions is an essential step in drug development. Most studies focus on predicting whether a drug pair interacts or is effective on a certain disease without considering the mechanism of action (MoA). Here, we introduce a novel method to infer effects and interactions of drug pairs with MoA based on the profiling of systemic effects of drugs. By investigating propagated drug effects from the molecular and phenotypic networks, we constructed profiles of 5,441 approved and investigational drugs for 3,833 phenotypes. Our analysis indicates that highly connected phenotypes between drug profiles represent the potential effects of drug pairs and the drug pairs with strong potential effects are more likely to interact. When applied to drug interactions with verified effects, both therapeutic and adverse effects have been successfully identified with high specificity and sensitivity. Finally, tracing drug interactions in molecular and phenotypic networks allows us to understand the MoA.},
keywords = {ADR, Bioinformatics, Drugs, Network analysis},
pubstate = {published},
tppubtype = {article}
}
Identifying unexpected drug interactions is an essential step in drug development. Most studies focus on predicting whether a drug pair interacts or is effective on a certain disease without considering the mechanism of action (MoA). Here, we introduce a novel method to infer effects and interactions of drug pairs with MoA based on the profiling of systemic effects of drugs. By investigating propagated drug effects from the molecular and phenotypic networks, we constructed profiles of 5,441 approved and investigational drugs for 3,833 phenotypes. Our analysis indicates that highly connected phenotypes between drug profiles represent the potential effects of drug pairs and the drug pairs with strong potential effects are more likely to interact. When applied to drug interactions with verified effects, both therapeutic and adverse effects have been successfully identified with high specificity and sensitivity. Finally, tracing drug interactions in molecular and phenotypic networks allows us to understand the MoA.