PUBLICATIONS
2018
4.
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.
3.
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.
2.
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.
2014
1.
Suhyun Ha; Sunyong Yoo; Moonshik Shin; Jin Sook Kwak; Oran Kwon; Min Chang Choi; Keon Wook Kang; Hojung Nam; Doheon Lee
Abstract | Links | BibTeX | Dimensions | Tags: Ethnopharmacology, Natural product
@conference{ha2014integrative,
title = {Integrative Database for Exploring Compound Combinations of Natural Products for Medical Effects},
author = {Suhyun Ha and Sunyong Yoo and Moonshik Shin and Jin Sook Kwak and Oran Kwon and Min Chang Choi and Keon Wook Kang and Hojung Nam and Doheon Lee},
url = {https://dl.acm.org/doi/abs/10.1145/2665970.2665986},
doi = {10.1145/2665970.2665986},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {Proceedings of the ACM 8th International Workshop on Data and Text Mining in Bioinformatics},
pages = {41–41},
publisher = {CIKM},
abstract = {Natural products used in dietary supplements, complementary and alternative medicine (CAM) and conventional medicine are composites of multiple chemical compounds. These chemical compounds potentially offer an extensive source for drug discovery with accumulated knowledge of efficacy and safety. However, existing natural product related databases have drawbacks in both standardization and structuralization of information. Therefore, in this work, we construct an integrated database of natural products by mapping the prescription, herb, compound, and phenotype information to international identifiers and structuralizing the efficacy information through database integration and text-mining methods. We expect that the constructed database could serve as a fundamental resource for the natural products research.},
keywords = {Ethnopharmacology, Natural product},
pubstate = {published},
tppubtype = {conference}
}
Natural products used in dietary supplements, complementary and alternative medicine (CAM) and conventional medicine are composites of multiple chemical compounds. These chemical compounds potentially offer an extensive source for drug discovery with accumulated knowledge of efficacy and safety. However, existing natural product related databases have drawbacks in both standardization and structuralization of information. Therefore, in this work, we construct an integrated database of natural products by mapping the prescription, herb, compound, and phenotype information to international identifiers and structuralizing the efficacy information through database integration and text-mining methods. We expect that the constructed database could serve as a fundamental resource for the natural products research.