JOURNALS
2025
4.
Hyejin Yu; Kwanyong Choi; Ji Yeon Kim; Sunyong Yoo
Abstract | Links | BibTeX | Dimensions | Tags: Artificial Intelligence, Bioinformatics, Ethnopharmacology, Herbal medicine, Network analysis
@article{Yu2025,
title = {Multi-level association rule mining and network pharmacology to identify the polypharmacological effects of herbal materials and compounds in traditional medicine},
author = {Hyejin Yu and Kwanyong Choi and Ji Yeon Kim and Sunyong Yoo},
url = {https://academic.oup.com/bib/article/26/4/bbaf328/8190205?utm_source=advanceaccess&utm_campaign=bib&utm_medium=email},
doi = {10.1093/bib/bbaf328},
issn = {1477-4054},
year = {2025},
date = {2025-07-01},
urldate = {2025-07-01},
journal = {Briefings in Bioinformatics},
volume = {26},
issue = {4},
abstract = {Many cultures worldwide have widely used traditional medicine (TM) to prevent or treat diseases. Herbal materials and their compounds used in TM offer many advantages for drug discovery, including cost-effectiveness, fewer side effects, and improved metabolism. However, the multi-compound and multi-target characteristics of TM prescriptions complicate drug discovery; meanwhile, previous studies have been limited by a lack of high-quality data, complex interpretation, and/or narrow analytical ranges. Thus, this study proposed a framework to identify potential therapeutic combinations of herbal materials and their compounds currently used in TM by integrating association rule mining (ARM) and network pharmacology analysis across multiple TM and biological levels. Subsequently, we collected prescriptions, herbal materials, compounds, genes, phenotypes, and all ensuing interactions to identify effective combinations of herbal materials and compounds using ARM for various symptoms and diseases. This proposed analytical approach was also applied to screen effective herbal material combinations and compounds for five phenotypes: asthma, diabetes, arthritis, stroke, and inflammation. The potential pharmacological effects of the inferred candidates were identified at the molecular level using structural network analysis and a literature review. In addition, compounds from Morus alba, Ephedra sinica, Perilla frutescens, and Pinellia ternata, which were strongly associated with asthma, were validated in vitro. Collectively, our study provides ethnopharmacological and biological evidence for the polypharmacological effects of herbal materials and their compounds, thus enhancing the understanding of the mechanisms involved in TM and suggesting potential candidates for prescriptions, dietary supplements, and drug combinations. The source code and results are available at https://github.com/bmil-jnu/InPETM.},
note = {Correspondence to Sunyong Yoo},
keywords = {Artificial Intelligence, Bioinformatics, Ethnopharmacology, Herbal medicine, Network analysis},
pubstate = {published},
tppubtype = {article}
}
Many cultures worldwide have widely used traditional medicine (TM) to prevent or treat diseases. Herbal materials and their compounds used in TM offer many advantages for drug discovery, including cost-effectiveness, fewer side effects, and improved metabolism. However, the multi-compound and multi-target characteristics of TM prescriptions complicate drug discovery; meanwhile, previous studies have been limited by a lack of high-quality data, complex interpretation, and/or narrow analytical ranges. Thus, this study proposed a framework to identify potential therapeutic combinations of herbal materials and their compounds currently used in TM by integrating association rule mining (ARM) and network pharmacology analysis across multiple TM and biological levels. Subsequently, we collected prescriptions, herbal materials, compounds, genes, phenotypes, and all ensuing interactions to identify effective combinations of herbal materials and compounds using ARM for various symptoms and diseases. This proposed analytical approach was also applied to screen effective herbal material combinations and compounds for five phenotypes: asthma, diabetes, arthritis, stroke, and inflammation. The potential pharmacological effects of the inferred candidates were identified at the molecular level using structural network analysis and a literature review. In addition, compounds from Morus alba, Ephedra sinica, Perilla frutescens, and Pinellia ternata, which were strongly associated with asthma, were validated in vitro. Collectively, our study provides ethnopharmacological and biological evidence for the polypharmacological effects of herbal materials and their compounds, thus enhancing the understanding of the mechanisms involved in TM and suggesting potential candidates for prescriptions, dietary supplements, and drug combinations. The source code and results are available at https://github.com/bmil-jnu/InPETM.
2018
3.
Sunyong Yoo; Suhyun Ha; Moonshik Shin; Kyungrin Noh; Hojung Nam; Doheon Lee
Abstract | Links | BibTeX | Dimensions | Tags: Database, Drugs, Ethnopharmacology, Machine learning
@article{yoo2018data,
title = {A data-driven approach for identifying medicinal combinations of natural products},
author = {Sunyong Yoo and Suhyun Ha and Moonshik Shin and Kyungrin Noh and Hojung Nam and Doheon Lee},
url = {https://ieeexplore.ieee.org/abstract/document/8482294},
doi = {10.1109/ACCESS.2018.2874089},
year = {2018},
date = {2018-10-05},
urldate = {2018-10-05},
journal = {IEEE Access},
volume = {6},
pages = {58106–58118},
publisher = {IEEE},
abstract = {Combinations of natural products have been used as important sources of disease treatments. Existing databases contain information about prescriptions, herbs, and compounds and their relationships with phenotypes, but they do not have information on the use of combinations of natural product compounds. In this paper, we identified large-scale associations between natural product combinations and phenotypes by applying an association rule mining technique to integrated information on herbal medicine, combination drugs, functional foods, molecular compounds, and target genes. The rationale behind this approach is that natural products commonly found in medicinal multicomponent mixtures have statistically significant associations with the therapeutic effects of the multicomponent mixtures. Based on a molecular network analysis and an external literature validation, we show that the inferred associations are valuable information for identifying medicinal combinations of natural products since they have statistically significant closeness proximity in the molecular layer and have much experimental evidence. All results are available through the workbench site at http://biosoft.kaist.ac.kr/coconut to facilitate the investigation of the medicinal use of natural products and their combinations.},
keywords = {Database, Drugs, Ethnopharmacology, Machine learning},
pubstate = {published},
tppubtype = {article}
}
Combinations of natural products have been used as important sources of disease treatments. Existing databases contain information about prescriptions, herbs, and compounds and their relationships with phenotypes, but they do not have information on the use of combinations of natural product compounds. In this paper, we identified large-scale associations between natural product combinations and phenotypes by applying an association rule mining technique to integrated information on herbal medicine, combination drugs, functional foods, molecular compounds, and target genes. The rationale behind this approach is that natural products commonly found in medicinal multicomponent mixtures have statistically significant associations with the therapeutic effects of the multicomponent mixtures. Based on a molecular network analysis and an external literature validation, we show that the inferred associations are valuable information for identifying medicinal combinations of natural products since they have statistically significant closeness proximity in the molecular layer and have much experimental evidence. All results are available through the workbench site at http://biosoft.kaist.ac.kr/coconut to facilitate the investigation of the medicinal use of natural products and their combinations.
2.
Sunyong Yoo; Kwansoo Kim; Hojung Nam; Doheon Lee
Abstract | Links | BibTeX | Dimensions | Tags: Bioinformatics, Chemical property, Ethnopharmacology, Herbal medicine, Molecular analysis, Network analysis, Phytochemical
@article{yoo2018discovering,
title = {Discovering health benefits of phytochemicals with integrated analysis of the molecular network, chemical properties and ethnopharmacological evidence},
author = {Sunyong Yoo and Kwansoo Kim and Hojung Nam and Doheon Lee},
url = {https://www.mdpi.com/2072-6643/10/8/1042},
doi = {10.3390/nu10081042},
year = {2018},
date = {2018-08-08},
urldate = {2018-08-08},
journal = {Nutrients},
volume = {10},
number = {8},
pages = {1042},
publisher = {MDPI},
abstract = {Identifying the health benefits of phytochemicals is an essential step in drug and functional food development. While many in vitro screening methods have been developed to identify the health effects of phytochemicals, there is still room for improvement because of high cost and low productivity. Therefore, researchers have alternatively proposed in silico methods, primarily based on three types of approaches; utilizing molecular, chemical or ethnopharmacological information. Although each approach has its own strength in analyzing the characteristics of phytochemicals, previous studies have not considered them all together. Here, we apply an integrated in silico analysis to identify the potential health benefits of phytochemicals based on molecular analysis and chemical properties as well as ethnopharmacological evidence. From the molecular analysis, we found an average of 415.6 health effects for 591 phytochemicals. We further investigated ethnopharmacological evidence of phytochemicals and found that on average 129.1 (31%) of the predicted health effects had ethnopharmacological evidence. Lastly, we investigated chemical properties to confirm whether they are orally bio-available, drug available or effective on certain tissues. The evaluation results indicate that the health effects can be predicted more accurately by cooperatively considering the molecular analysis, chemical properties and ethnopharmacological evidence.},
keywords = {Bioinformatics, Chemical property, Ethnopharmacology, Herbal medicine, Molecular analysis, Network analysis, Phytochemical},
pubstate = {published},
tppubtype = {article}
}
Identifying the health benefits of phytochemicals is an essential step in drug and functional food development. While many in vitro screening methods have been developed to identify the health effects of phytochemicals, there is still room for improvement because of high cost and low productivity. Therefore, researchers have alternatively proposed in silico methods, primarily based on three types of approaches; utilizing molecular, chemical or ethnopharmacological information. Although each approach has its own strength in analyzing the characteristics of phytochemicals, previous studies have not considered them all together. Here, we apply an integrated in silico analysis to identify the potential health benefits of phytochemicals based on molecular analysis and chemical properties as well as ethnopharmacological evidence. From the molecular analysis, we found an average of 415.6 health effects for 591 phytochemicals. We further investigated ethnopharmacological evidence of phytochemicals and found that on average 129.1 (31%) of the predicted health effects had ethnopharmacological evidence. Lastly, we investigated chemical properties to confirm whether they are orally bio-available, drug available or effective on certain tissues. The evaluation results indicate that the health effects can be predicted more accurately by cooperatively considering the molecular analysis, chemical properties and ethnopharmacological evidence.
1.
Sunyong Yoo; Hojung Nam; Doheon Lee
Abstract | Links | BibTeX | Dimensions | Tags: Ethnopharmacology, Natural product, Network analysis
@article{yoo2018phenotype,
title = {Phenotype-oriented network analysis for discovering pharmacological effects of natural compounds},
author = {Sunyong Yoo and Hojung Nam and Doheon Lee},
url = {https://www.nature.com/articles/s41598-018-30138-w},
doi = {10.1038/s41598-018-30138-w},
year = {2018},
date = {2018-08-03},
urldate = {2018-08-03},
journal = {Scientific Reports},
volume = {8},
number = {1},
pages = {11667},
publisher = {Nature Publishing Group UK London},
abstract = {Although natural compounds have provided a wealth of leads and clues in drug development, the process of identifying their pharmacological effects is still a challenging task. Over the last decade, many in vitro screening methods have been developed to identify the pharmacological effects of natural compounds, but they are still costly processes with low productivity. Therefore, in silico methods, primarily based on molecular information, have been proposed. However, large-scale analysis is rarely considered, since many natural compounds do not have molecular structure and target protein information. Empirical knowledge of medicinal plants can be used as a key resource to solve the problem, but this information is not fully exploited and is used only as a preliminary tool for selecting plants for specific diseases. Here, we introduce a novel method to identify pharmacological effects of natural compounds from herbal medicine based on phenotype-oriented network analysis. In this study, medicinal plants with similar efficacy were clustered by investigating hierarchical relationships between the known efficacy of plants and 5,021 phenotypes in the phenotypic network. We then discovered significantly enriched natural compounds in each plant cluster and mapped the averaged pharmacological effects of the plant cluster to the natural compounds. This approach allows us to predict unexpected effects of natural compounds that have not been found by molecular analysis. When applied to verified medicinal compounds, our method successfully identified their pharmacological effects with high specificity and sensitivity.},
keywords = {Ethnopharmacology, Natural product, Network analysis},
pubstate = {published},
tppubtype = {article}
}
Although natural compounds have provided a wealth of leads and clues in drug development, the process of identifying their pharmacological effects is still a challenging task. Over the last decade, many in vitro screening methods have been developed to identify the pharmacological effects of natural compounds, but they are still costly processes with low productivity. Therefore, in silico methods, primarily based on molecular information, have been proposed. However, large-scale analysis is rarely considered, since many natural compounds do not have molecular structure and target protein information. Empirical knowledge of medicinal plants can be used as a key resource to solve the problem, but this information is not fully exploited and is used only as a preliminary tool for selecting plants for specific diseases. Here, we introduce a novel method to identify pharmacological effects of natural compounds from herbal medicine based on phenotype-oriented network analysis. In this study, medicinal plants with similar efficacy were clustered by investigating hierarchical relationships between the known efficacy of plants and 5,021 phenotypes in the phenotypic network. We then discovered significantly enriched natural compounds in each plant cluster and mapped the averaged pharmacological effects of the plant cluster to the natural compounds. This approach allows us to predict unexpected effects of natural compounds that have not been found by molecular analysis. When applied to verified medicinal compounds, our method successfully identified their pharmacological effects with high specificity and sensitivity.