Compound-level identification of sasang constitution type-specific personalized herbal medicine using data science approach
Introduction: Sasang Constitutional Medicine (SCM) is a type of traditional Korean medicine where patients are classified as one of four Sasang constitution types (Sasang type) and medications consisting of medicinal herbs are prescribed according to the Sasang type. Despite the importance of person...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402300899X |
| _version_ | 1811161722986168320 |
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| author | Sa-Yoon Park Young Woo Kim Yu Rim Song Seon Been Bak Young Pyo Jang Il-Kon Kim Ji-Hwan Kim Chang-Eop Kim |
| author_facet | Sa-Yoon Park Young Woo Kim Yu Rim Song Seon Been Bak Young Pyo Jang Il-Kon Kim Ji-Hwan Kim Chang-Eop Kim |
| author_sort | Sa-Yoon Park |
| collection | DOAJ |
| description | Introduction: Sasang Constitutional Medicine (SCM) is a type of traditional Korean medicine where patients are classified as one of four Sasang constitution types (Sasang type) and medications consisting of medicinal herbs are prescribed according to the Sasang type. Despite the importance of personalized medicine, the operation mechanism is largely unknown. To gain a better understanding, we investigated the compound information that composes Sasang type-specific personalized herbal medicines on both multivariate and univariate levels. Methods: Five machine learning classifiers including extremely randomized trees (ERT) were trained to investigate whether the Sasang type can be explained by compound information at the multivariate level. Hierarchical clustering was conducted to determine whether compounds are processed distributedly or specifically. Taxonomic and biosynthetic analyses were conducted on these compounds. A univariate level statistical test was conducted to provide more robust Sasang type-specific compound information. Results: Using the trained ERT classifier, sixty important compounds were extracted. The sixty compounds were clustered into three groups, corresponding to each Sasang type-prominent compounds, suggesting that most compounds have specific preference for the Sasang type. Structural and biosynthetic characteristics of these Sasang type-prominent compounds were determined based on taxonomy and pathway analyses. Fourteen compounds showed statistically significant relevance with the Sasang type. Additionally, we predicted the Sasang type of unknown herbs, which were confirmed by their biological effects in functional assays. Conclusion: This study investigated the personalized herbal medicines of the SCM using compound information. This study provided information on the chemical characteristics of the compounds that are essential for classifying the Sasang type of medicinal herbs, as well as predictions regarding the Sasang type of the commonly used but unidentified medicinal herbs. |
| first_indexed | 2024-04-10T06:20:06Z |
| format | Article |
| id | doaj.art-f28b5a5a7e124d3abed1f5ec0326c30d |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-10T06:20:06Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-f28b5a5a7e124d3abed1f5ec0326c30d2023-03-02T05:02:31ZengElsevierHeliyon2405-84402023-02-0192e13692Compound-level identification of sasang constitution type-specific personalized herbal medicine using data science approachSa-Yoon Park0Young Woo Kim1Yu Rim Song2Seon Been Bak3Young Pyo Jang4Il-Kon Kim5Ji-Hwan Kim6Chang-Eop Kim7Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, 13120, Republic of KoreaDepartment of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, Republic of Korea; School of Korean Medicine, Dongguk University, Gyeongju, 38066, Republic of KoreaSchool of Korean Medicine, Dongguk University, Gyeongju, 38066, Republic of KoreaSchool of Korean Medicine, Dongguk University, Gyeongju, 38066, Republic of KoreaCollege of Pharmacy, Kyung Hee University, Seoul, 02447, South KoreaDepartment of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, Republic of KoreaDepartment of Sasang Constitutional Medicine, Gil Hospital of Korean Medicine, Gachon University, Incheon, 21565, Republic of Korea; Corresponding author.Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, 13120, Republic of Korea; Corresponding author.Introduction: Sasang Constitutional Medicine (SCM) is a type of traditional Korean medicine where patients are classified as one of four Sasang constitution types (Sasang type) and medications consisting of medicinal herbs are prescribed according to the Sasang type. Despite the importance of personalized medicine, the operation mechanism is largely unknown. To gain a better understanding, we investigated the compound information that composes Sasang type-specific personalized herbal medicines on both multivariate and univariate levels. Methods: Five machine learning classifiers including extremely randomized trees (ERT) were trained to investigate whether the Sasang type can be explained by compound information at the multivariate level. Hierarchical clustering was conducted to determine whether compounds are processed distributedly or specifically. Taxonomic and biosynthetic analyses were conducted on these compounds. A univariate level statistical test was conducted to provide more robust Sasang type-specific compound information. Results: Using the trained ERT classifier, sixty important compounds were extracted. The sixty compounds were clustered into three groups, corresponding to each Sasang type-prominent compounds, suggesting that most compounds have specific preference for the Sasang type. Structural and biosynthetic characteristics of these Sasang type-prominent compounds were determined based on taxonomy and pathway analyses. Fourteen compounds showed statistically significant relevance with the Sasang type. Additionally, we predicted the Sasang type of unknown herbs, which were confirmed by their biological effects in functional assays. Conclusion: This study investigated the personalized herbal medicines of the SCM using compound information. This study provided information on the chemical characteristics of the compounds that are essential for classifying the Sasang type of medicinal herbs, as well as predictions regarding the Sasang type of the commonly used but unidentified medicinal herbs.http://www.sciencedirect.com/science/article/pii/S240584402300899XPersonalized herbal medicineSasang constitutional medicineChemical characteristicsCompound information |
| spellingShingle | Sa-Yoon Park Young Woo Kim Yu Rim Song Seon Been Bak Young Pyo Jang Il-Kon Kim Ji-Hwan Kim Chang-Eop Kim Compound-level identification of sasang constitution type-specific personalized herbal medicine using data science approach Heliyon Personalized herbal medicine Sasang constitutional medicine Chemical characteristics Compound information |
| title | Compound-level identification of sasang constitution type-specific personalized herbal medicine using data science approach |
| title_full | Compound-level identification of sasang constitution type-specific personalized herbal medicine using data science approach |
| title_fullStr | Compound-level identification of sasang constitution type-specific personalized herbal medicine using data science approach |
| title_full_unstemmed | Compound-level identification of sasang constitution type-specific personalized herbal medicine using data science approach |
| title_short | Compound-level identification of sasang constitution type-specific personalized herbal medicine using data science approach |
| title_sort | compound level identification of sasang constitution type specific personalized herbal medicine using data science approach |
| topic | Personalized herbal medicine Sasang constitutional medicine Chemical characteristics Compound information |
| url | http://www.sciencedirect.com/science/article/pii/S240584402300899X |
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