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Zhu J, Liu X, Gao P. Digital intelligence technology: new quality productivity for precision traditional Chinese medicine. Front Pharmacol 2025; 16:1526187. [PMID: 40264673 PMCID: PMC12012302 DOI: 10.3389/fphar.2025.1526187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2024] [Accepted: 03/20/2025] [Indexed: 04/24/2025] Open
Abstract
Traditional Chinese medicine is a complex medical system characterized by multiple metabolites, targets, and pathways, known for its low drug resistance and significant efficacy. However, challenges persist within Traditional Chinese medicine, including difficulties in assessing the quality of Botanical drugs, reliance on experiential knowledge for disease diagnosis and treatment, and a lack of clarity regarding the pharmacological mechanisms of Traditional Chinese medicine. The advancement of digital intelligence technology is driving a shift towards precision medicine within the Traditional Chinese medicine model. This transition propels Traditional Chinese medicine into an era of precision, intelligence, and digitalization. This paper introduces standard digital intelligence technologies and explores the application of digital intelligence technologies in quality control and evaluation of Traditional Chinese medicine, studies the research status of digital intelligence technologies in assisting diagnosis, treatment and prevention of diseases, and further promotes the application and development of digital intelligence technologies in the field of Traditional Chinese medicine.
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Affiliation(s)
| | - Xiaonan Liu
- Shandong Key Laboratory of Digital Traditional Chinese Medicine, Institute of Pharmaceutical Research, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Peng Gao
- Shandong Key Laboratory of Digital Traditional Chinese Medicine, Institute of Pharmaceutical Research, Shandong University of Traditional Chinese Medicine, Jinan, China
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Pan P, Chen W, Wu X, Li C, Gao Y, Qin D. Active Targets and Potential Mechanisms of Erhuang Quzhi Formula in Treating NAFLD: Network Analysis and Experimental Assessment. Cell Biochem Biophys 2024; 82:3297-3315. [PMID: 39120856 DOI: 10.1007/s12013-024-01413-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/04/2024] [Indexed: 08/10/2024]
Abstract
The purpose of this research was to investigate the main active components, potential targets of action, and pharmacological mechanisms of Erhuang Quzhi Formula (EHQZF) against NAFLD using network pharmacology, molecular docking, and experimental validation. The main active chemical components of EHQZF and the potential targets for treating NAFLD were extracted and analyzed. The PPI network diagram of "Traditional Chinese Medicine-Active Ingredients-Core Targets" was constructed and the GO, KEGG, and molecular docking analysis were carried out. Identification of components in traditional Chinese medicine compounds was conducted by LC-MS. NAFLD models were established and relevant pathologic indicators and Western blot were analyzed in vivo and ex vivo. Totally 8 herbs attributed to the liver meridian and 20 corresponding targets of NAFLD were obtained from EHQZF. Flavonoids and phenolic acids as the main components of EHQZF treated NAFLD through the MAPK/AKT signaling pathway. Pathway enrichment analysis focused on the MAPK/AKT signaling pathway and apoptosis signaling pathway. Molecular docking showed that Quercetin and Luteolin had stable binding structures with AKT1, STAT3, and other targets. Experiments showed that EHQZF reduced lipid accumulation, regulated changes in adipose tissue, inhibited the MAPK/AKT signaling pathway and exert multiple components, several targets, and multiple pathway interactions to treat NAFLD.
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Affiliation(s)
- Peiyan Pan
- Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, School of Pharmacy, Shihezi University, Shihezi, China
| | - Weijun Chen
- Xinjiang Second Medical College, Karamay, China
| | - Xi Wu
- Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Cong Li
- Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, School of Pharmacy, Shihezi University, Shihezi, China
| | - Yuefeng Gao
- College of Applied Engineering, Henan University of Science and Technology, Sanmenxia, China
| | - Dongmei Qin
- Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, School of Pharmacy, Shihezi University, Shihezi, China.
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Liang D, Yixuan D, Chang L, Jingjing S, Sihai Z, Jie D. Mechanism of Artemisia annua L. in the treatment of acute myocardial infarction: network pharmacology, molecular docking and in vivo validation. Mol Divers 2024; 28:3225-3242. [PMID: 37898972 DOI: 10.1007/s11030-023-10750-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 10/14/2023] [Indexed: 10/31/2023]
Abstract
This study was to evaluate the potential mechanism of action of Artemisia annua L. (A. annua) in the treatment of acute myocardial infarction (AMI) using network pharmacology, molecular docking and in vivo experiments. 22 active chemical compounds and 193 drug targets of A. annua were screened using the Traditional Chinese Medicine System Pharmacological (TCMSP) database. 3876 disease targets were also collected. Then 158 intersection targets between AMI and A. annua were obtained using R 4.2.0 software. String database was used to construct the protein-protein interaction (PPI) network and 6 core targets (MAPK1, TP53, HSP90AA1, RELA, AKT1, and MYC) were screened. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the R package. GO enrichment results were mainly related to cell responses to chemical stress and cell membrane microregions. KEGG pathways were mainly involved in lipids, atherosclerosis and fluid shear stress. In addition, molecular docking between A. annua active compounds and core targets showed high binding activity. As for in vivo validation, A. annua extract showed significant effects on improving post-infarction ventricular function, delaying ventricular remodeling, and reducing myocardial fibrosis and apoptosis. This study has revealed the potential components and molecular mechanisms of A. annua in the treatment of AMI. Our work also showed that A. annua has great effect on reducing myocardial fibrosis and scar area after infarction.
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Affiliation(s)
- Deng Liang
- School of Medicine, Shanxi Datong University, Datong, 037009, Shanxi, China
| | - Duan Yixuan
- Department of Cardiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China
| | - Liu Chang
- Department of Cardiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China
| | - Sun Jingjing
- Department of Cardiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China
| | - Zhao Sihai
- Laboratory Animal Center, Xi'an Jiaotong University School of Medicine, Xi'an, 710061, Shaanxi, China
| | - Deng Jie
- Department of Cardiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China.
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Zeng J, Jia X. Systems Theory-Driven Framework for AI Integration into the Holistic Material Basis Research of Traditional Chinese Medicine. ENGINEERING 2024; 40:28-50. [DOI: 10.1016/j.eng.2024.04.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2025]
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Wang Y, Sui Y, Yao J, Jiang H, Tian Q, Tang Y, Ou Y, Tang J, Tan N. Herb-CMap: a multimodal fusion framework for deciphering the mechanisms of action in traditional Chinese medicine using Suhuang antitussive capsule as a case study. Brief Bioinform 2024; 25:bbae362. [PMID: 39073832 DOI: 10.1093/bib/bbae362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 06/21/2024] [Accepted: 07/13/2024] [Indexed: 07/30/2024] Open
Abstract
Herbal medicines, particularly traditional Chinese medicines (TCMs), are a rich source of natural products with significant therapeutic potential. However, understanding their mechanisms of action is challenging due to the complexity of their multi-ingredient compositions. We introduced Herb-CMap, a multimodal fusion framework leveraging protein-protein interactions and herb-perturbed gene expression signatures. Utilizing a network-based heat diffusion algorithm, Herb-CMap creates a connectivity map linking herb perturbations to their therapeutic targets, thereby facilitating the prioritization of active ingredients. As a case study, we applied Herb-CMap to Suhuang antitussive capsule (Suhuang), a TCM formula used for treating cough variant asthma (CVA). Using in vivo rat models, our analysis established the transcriptomic signatures of Suhuang and identified its key compounds, such as quercetin and luteolin, and their target genes, including IL17A, PIK3CB, PIK3CD, AKT1, and TNF. These drug-target interactions inhibit the IL-17 signaling pathway and deactivate PI3K, AKT, and NF-κB, effectively reducing lung inflammation and alleviating CVA. The study demonstrates the efficacy of Herb-CMap in elucidating the molecular mechanisms of herbal medicines, offering valuable insights for advancing drug discovery in TCM.
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Affiliation(s)
- Yinyin Wang
- Department of TCMs Pharmaceuticals, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 639 Longmian Avenue, Nanjing 211198, PR China
| | - Yihang Sui
- Department of TCMs Pharmaceuticals, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 639 Longmian Avenue, Nanjing 211198, PR China
| | - Jiaqi Yao
- Department of TCMs Pharmaceuticals, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 639 Longmian Avenue, Nanjing 211198, PR China
| | - Hong Jiang
- Department of TCMs Pharmaceuticals, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 639 Longmian Avenue, Nanjing 211198, PR China
| | - Qimeng Tian
- Department of TCMs Pharmaceuticals, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 639 Longmian Avenue, Nanjing 211198, PR China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, No. 130 Meilong Road, Shanghai 200237, China
| | - Yongyu Ou
- Beijing Haiyan Pharmaceutical Co., Ltd., Yangtze River Pharmaceutical Group, No. 16 Shengmingyuan Road, Beijing 102206, PR China
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Haartmaninkatu 8, Helsinki FI-00290, Finland
| | - Ninghua Tan
- Department of TCMs Pharmaceuticals, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 639 Longmian Avenue, Nanjing 211198, PR China
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Lee YS, Chae Y. Pattern Identification and Acupuncture Prescriptions Based on Real-World Data Using Artificial Intelligence. EAST ASIAN SCIENCE, TECHNOLOGY AND SOCIETY: AN INTERNATIONAL JOURNAL 2024:1-18. [DOI: 10.1080/18752160.2024.2339657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 01/26/2024] [Indexed: 01/04/2025]
Affiliation(s)
- Ye-Seul Lee
- Ye-Seul Lee Jaseng Spine and Joint Research Institute, Jaseng Medical Foundation, Seoul, Republic of Korea
| | - Younbyoung Chae
- Younbyoung Chae Acupuncture & Meridian Science Research Center, Kyung Hee University, Seoul, Republic of Korea
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Ng JY, Cramer H, Lee MS, Moher D. Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare. Integr Med Res 2024; 13:101024. [PMID: 38384497 PMCID: PMC10879672 DOI: 10.1016/j.imr.2024.101024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/02/2024] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
The convergence of traditional, complementary, and integrative medicine (TCIM) with artificial intelligence (AI) is a promising frontier in healthcare. TCIM is a patient-centric approach that combines conventional medicine with complementary therapies, emphasizing holistic well-being. AI can revolutionize healthcare through data-driven decision-making and personalized treatment plans. This article explores how AI technologies can complement and enhance TCIM, aligning with the shared objectives of researchers from both fields in improving patient outcomes, enhancing care quality, and promoting holistic wellness. This integration of TCIM and AI introduces exciting opportunities but also noteworthy challenges. AI may augment TCIM by assisting in early disease detection, providing personalized treatment plans, predicting health trends, and enhancing patient engagement. Challenges at the intersection of AI and TCIM include data privacy and security, regulatory complexities, maintaining the human touch in patient-provider relationships, and mitigating bias in AI algorithms. Patients' trust, informed consent, and legal accountability are all essential considerations. Future directions in AI-enhanced TCIM include advanced personalized medicine, understanding the efficacy of herbal remedies, and studying patient-provider interactions. Research on bias mitigation, patient acceptance, and trust in AI-driven TCIM healthcare is crucial. In this article, we outlined that the merging of TCIM and AI holds great promise in enhancing healthcare delivery, personalizing treatment plans, preventive care, and patient engagement. Addressing challenges and fostering collaboration between AI experts, TCIM practitioners, and policymakers, however, is vital to harnessing the full potential of this integration.
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Affiliation(s)
- Jeremy Y. Ng
- Centre for Journalology, Ottawa Hospital Research Institute, Ottawa, Canada
- Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany
- Robert Bosch Center for Integrative Medicine and Health, Bosch Health Campus, Stuttgart, Germany
| | - Holger Cramer
- Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany
- Robert Bosch Center for Integrative Medicine and Health, Bosch Health Campus, Stuttgart, Germany
| | - Myeong Soo Lee
- KM Science Research Division, Korea Institute of Oriental Medicine, Daejeon, South Korea
| | - David Moher
- Centre for Journalology, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
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Zhang S, Zhang X, Du J, Wang W, Pi X. Multi-target meridians classification based on the topological structure of anti-cancer phytochemicals using deep learning. JOURNAL OF ETHNOPHARMACOLOGY 2024; 319:117244. [PMID: 37777031 DOI: 10.1016/j.jep.2023.117244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/24/2023] [Accepted: 09/27/2023] [Indexed: 10/02/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Traditional Chinese medicine (TCM) meridian is the key theoretical guidance of prescription against tumor in clinical practice. However, there is no scientific and systematic verification of therapeutic action of herbs under meridians context. Several studies have determined the Chinese herbal medicine (CHM) phytochemicals for intrinsic attribute or meridians classification based on artificial intelligence (AI) tools. However, it is challenging to represent the complex molecular structures with large heterogeneity through the current technologies. In addition, the multiple correspondence between herbs and meridians has not been paid much attention. AIM OF THE STUDY We aim to develop an AI framework to classify multi-target meridians through the topological structure of phytochemicals. MATERIALS AND METHODS A total of 354 anti-cancer herbs, their corresponding TCM meridians and 5471 ingredient compounds were collected from public databases of CancerHSP, ETCM, and Hit 2.0. The statistical analysis of herbal and compound datasets, clustering analysis of the associated cancers, and correlational analysis of meridian tropism were preliminary conducted. Then a deep learning (DL) hybrid model named GRMC consisting of graph convolutional network (GCN) and recurrent neural network (RNN) was employed to generate the meridian multi-label sequences based on molecular graph. RESULTS The curing herbs against tumors have tight relationships to lung, liver, stomach, and spleen meridians. These herbs behave different properties in curing certain cancer. Certain cancer types have co-occurrence such as ovarian, bladder and cervical cancer. Compounds have multitarget meridians with characteristics of higher-order correlations. Compared with the other state-of-the-art algorithms on the datasets and previous methods dealing with conventional fixed fingerprints of herbal compounds, the proposed GRMC has superior overall performance on testing dataset with the one error of 0.183, hamming loss of 0.112, mean averaged accuracy (MAA) of 0.855, mean averaged precision (MAP) of 0.891, mean averaged recall (MAR) of 0.812, and mean averaged F1 score (MAF) of 0.849. CONCLUSIONS The proposed method can predict multi-targeted meridians through neural graph features in herbal compounds and outperforms several comparison methods. It could provide a basis for understanding the molecular scientific evidence of TCM meridians.
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Affiliation(s)
- Sheng Zhang
- Key Laboratory of Biorheological Science and Technology of Ministry of Education, College of Bioengineering, Chongqing University, No.174 Shazheng Road, Shapingba District, Chongqing, 400044, PR China.
| | - Xianwei Zhang
- Key Laboratory of Biorheological Science and Technology of Ministry of Education, College of Bioengineering, Chongqing University, No.174 Shazheng Road, Shapingba District, Chongqing, 400044, PR China.
| | - Jiayin Du
- School of Pharmacy, Chongqing University, Chongqing, 400044, PR China.
| | - Wei Wang
- Department of Cardiology, Chongqing University Cancer Hospital, No. 181 Hanyu Road, Shapingba District, Chongqing, 400030, PR China.
| | - Xitian Pi
- Key Laboratory of Biorheological Science and Technology of Ministry of Education, College of Bioengineering, Chongqing University, No.174 Shazheng Road, Shapingba District, Chongqing, 400044, PR China.
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Hsiung SY, Deng SX, Li J, Huang SY, Liaw CK, Huang SY, Wang CC, Hsieh YSY. Machine learning-based monosaccharide profiling for tissue-specific classification of Wolfiporia extensa samples. Carbohydr Polym 2023; 322:121338. [PMID: 37839831 DOI: 10.1016/j.carbpol.2023.121338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 08/09/2023] [Accepted: 08/26/2023] [Indexed: 10/17/2023]
Abstract
Machine learning (ML) has been used for many clinical decision-making processes and diagnostic procedures in bioinformatics applications. We examined eight algorithms, including linear discriminant analysis (LDA), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), Naïve Bayes classifier (NB), and artificial neural network (ANN) models, to evaluate their classification and prediction capabilities for four tissue types in Wolfiporia extensa using their monosaccharide composition profiles. All 8 ML-based models were assessed as exemplary models with AUC exceeding 0.8. Five models, namely LDA, KNN, RF, GBM, and ANN, performed excellently in the four-tissue-type classification (AUC > 0.9). Additionally, all eight models were evaluated as good predictive models with AUC value > 0.8 in the three-tissue-type classification. Notably, all 8 ML-based methods outperformed the single linear discriminant analysis (LDA) plotting method. For large sample sizes, the ML-based methods perform better than traditional regression techniques and could potentially increase the accuracy in identifying tissue samples of W. extensa.
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Affiliation(s)
- Shih-Yi Hsiung
- School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan
| | - Shun-Xin Deng
- School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan; Graduate Institute of Pharmacognosy, Taipei Medical University, Taipei, Taiwan
| | - Jing Li
- College of Life Science, Shanghai Normal University, Shanghai, China
| | - Sheng-Yao Huang
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Chen-Kun Liaw
- Department of Orthopedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Su-Yun Huang
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Ching-Chiung Wang
- School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan; Graduate Institute of Pharmacognosy, Taipei Medical University, Taipei, Taiwan
| | - Yves S Y Hsieh
- School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan; Graduate Institute of Pharmacognosy, Taipei Medical University, Taipei, Taiwan; Division of Glycoscience, Department of Chemistry, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, AlbaNova University Centre, Stockholm SE106 91, Sweden.
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Gayathiri E, Prakash P, Kumaravel P, Jayaprakash J, Ragunathan MG, Sankar S, Pandiaraj S, Thirumalaivasan N, Thiruvengadam M, Govindasamy R. Computational approaches for modeling and structural design of biological systems: A comprehensive review. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 185:17-32. [PMID: 37821048 DOI: 10.1016/j.pbiomolbio.2023.08.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 08/14/2023] [Accepted: 08/27/2023] [Indexed: 10/13/2023]
Abstract
The convergence of biology and computational science has ushered in a revolutionary era, revolutionizing our understanding of biological systems and providing novel solutions to global problems. The field of genetic engineering has facilitated the manipulation of genetic codes, thus providing opportunities for the advancement of innovative disease therapies and environmental enhancements. The emergence of bio-molecular simulation represents a significant advancement in this particular field, as it offers the ability to gain microscopic insights into molecular-level biological processes over extended periods. Biomolecular simulation plays a crucial role in advancing our comprehension of organismal mechanisms by establishing connections between molecular structures, interactions, and biological functions. The field of computational biology has demonstrated its significance in deciphering intricate biological enigmas through the utilization of mathematical models and algorithms. The process of decoding the human genome has resulted in the advancement of therapies for a wide range of genetic disorders, while the simulation of biological systems contributes to the identification of novel pharmaceutical compounds. The potential of biomolecular simulation and computational biology is vast and limitless. As the exploration of the underlying principles that govern living organisms progresses, the potential impact of this understanding on cancer treatment, environmental restoration, and other domains is anticipated to be transformative. This review examines the notable advancements achieved in the field of computational biology, emphasizing its potential to revolutionize the comprehension and enhancement of biological systems.
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Affiliation(s)
- Ekambaram Gayathiri
- Department of Plant Biology and Plant Biotechnology, Guru Nanak College (Autonomous), Chennai, 42, Tamil Nadu, India
| | - Palanisamy Prakash
- Department of Botany, Periyar University, Periyar Palkalai Nagar, Salem, 636011, Tamil Nadu, India
| | - Priya Kumaravel
- Department of Biotechnology, St. Joseph College (Arts & Science), Kovur, Chennai, Tamil Nadu, India
| | - Jayanthi Jayaprakash
- Department of Advanced Zoology and Biotechnology, Guru Nanak College, Chennai, Tamil Nadu, India
| | | | - Sharmila Sankar
- Department of Advanced Zoology and Biotechnology, Guru Nanak College, Chennai, Tamil Nadu, India
| | - Saravanan Pandiaraj
- Department of Self-Development Skills, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia
| | - Natesan Thirumalaivasan
- Department of Periodontics, Saveetha Dental College, and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMTAS), Chennai, 600077, Tamil Nadu, India
| | - Muthu Thiruvengadam
- Department of Applied Bioscience, College of Life and Environmental Sciences, Konkuk University, Seoul, 05029, South Korea
| | - Rajakumar Govindasamy
- Department of Orthodontics, Saveetha Dental College and Hospitals, Saveetha University, Chennai, India.
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Jin W, Tao Y, Wang C, Wang L, Ao X, Su M, Hu B, Ouyang Y, Liu J, Li H. Infrared Imageries of Human Body Activated by Tea Match the Hypothesis of Meridian System. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:502-518. [PMID: 37881315 PMCID: PMC10593733 DOI: 10.1007/s43657-022-00090-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 11/21/2022] [Accepted: 11/25/2022] [Indexed: 10/27/2023]
Abstract
Human meridian (Jingluo) system was hypothesized by traditional Chinese medicine (TCM) for thousands of years, suggesting 12 normal meridian channels going through respective organs, carrying fluid and energy, and laying thermal effects. Some treatments based on meridians have been proved effective. However, existence of meridians has never been confirmed, let alone the lack of measurement for meridian phenotypes. Thermal effect is one of the major phenotypes of meridian metabolism. Infrared photograph was employed to display the picture of meridians since 1970. Unfortunately, no satisfactory results have been obtained. It is possible that only when a certain meridian is activated will there be thermal effect for successful infrared photograph. In this study, 13 types of tea were selected out of the herbs to activate the hypothesized 12 meridians for imagery taking. Forty-two volunteers took part in the experiment lasted for 13 days. Different tea was tested in different day. Infrared imageries of the human bodies were taken immediately after each tea was drunk. The highest temperatures of the fingers, palms, and above the organs were derived from the imageries and analyzed. The temperatures of the organs and fingers possibly connected by 12 hypothesized meridians rose together significantly following the meridian hypothesis. Infrared imageries showed quite clear shapes of the organs activated by different kinds of tea, e.g., heart and kidneys by yellow tea, etc. Some high temperature lines also matched the hypothetic meridians. Our work displayed the probable imageries of all the 12 hypothetic meridians for the first time, and proved with data that different foods may activate different organs following the meridian hypothesis, shedding light on a possible new method of targeted drug designs. Measurements of meridian phenotypes can be developed based on this method of activation. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-022-00090-x.
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Affiliation(s)
- Wenli Jin
- Shanghai Natural History Museum, Shanghai Science and Technology Museum, Shanghai, 200041 China
| | - Yichen Tao
- MOE Key Laboratory of Contemporary Anthropology, Human Phenome Institute, Fudan University, Shanghai, 200438 China
| | - Chen Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, 200438 China
| | - Lufei Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, 200438 China
- Fudan-Datong Institute of Chinese Origin, Shanxi Academy of Advanced Research and Innovation, Datong, 037006 China
| | - Xue Ao
- MOE Key Laboratory of Contemporary Anthropology, Human Phenome Institute, Fudan University, Shanghai, 200438 China
- Fudan-Datong Institute of Chinese Origin, Shanxi Academy of Advanced Research and Innovation, Datong, 037006 China
| | - Mingjie Su
- MOE Key Laboratory of Contemporary Anthropology, Human Phenome Institute, Fudan University, Shanghai, 200438 China
| | - Binwei Hu
- MOE Key Laboratory of Contemporary Anthropology, Human Phenome Institute, Fudan University, Shanghai, 200438 China
| | - Yuxiao Ouyang
- MOE Key Laboratory of Contemporary Anthropology, Human Phenome Institute, Fudan University, Shanghai, 200438 China
| | - Jiaxing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, 200438 China
| | - Hui Li
- MOE Key Laboratory of Contemporary Anthropology, Human Phenome Institute, Fudan University, Shanghai, 200438 China
- Fudan-Datong Institute of Chinese Origin, Shanxi Academy of Advanced Research and Innovation, Datong, 037006 China
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Ma S, Liu J, Li W, Liu Y, Hui X, Qu P, Jiang Z, Li J, Wang J. Machine learning in TCM with natural products and molecules: current status and future perspectives. Chin Med 2023; 18:43. [PMID: 37076902 PMCID: PMC10116715 DOI: 10.1186/s13020-023-00741-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/28/2023] [Indexed: 04/21/2023] Open
Abstract
Traditional Chinese medicine (TCM) has been practiced for thousands of years with clinical efficacy. Natural products and their effective agents such as artemisinin and paclitaxel have saved millions of lives worldwide. Artificial intelligence is being increasingly deployed in TCM. By summarizing the principles and processes of deep learning and traditional machine learning algorithms, analyzing the application of machine learning in TCM, reviewing the results of previous studies, this study proposed a promising future perspective based on the combination of machine learning, TCM theory, chemical compositions of natural products, and computational simulations based on molecules and chemical compositions. In the first place, machine learning will be utilized in the effective chemical components of natural products to target the pathological molecules of the disease which could achieve the purpose of screening the natural products on the basis of the pathological mechanisms they target. In this approach, computational simulations will be used for processing the data for effective chemical components, generating datasets for analyzing features. In the next step, machine learning will be used to analyze the datasets on the basis of TCM theories such as the superposition of syndrome elements. Finally, interdisciplinary natural product-syndrome research will be established by unifying the results of the two steps outlined above, potentially realizing an intelligent artificial intelligence diagnosis and treatment model based on the effective chemical components of natural products under the guidance of TCM theory. This perspective outlines an innovative application of machine learning in the clinical practice of TCM based on the investigation of chemical molecules under the guidance of TCM theory.
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Affiliation(s)
- Suya Ma
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China
| | - Jinlei Liu
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China
| | - Wenhua Li
- Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Yongmei Liu
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China
| | - Xiaoshan Hui
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China
| | - Peirong Qu
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China
| | - Zhilin Jiang
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China
| | - Jun Li
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China.
| | - Jie Wang
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China.
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Zhou J, Zhou B, Kou X, Jian T, Chen L, Lei X, Jia S, Xie X, Wu X. Effect of summer acupoint application treatment (SAAT) on gut microbiota in healthy Asian adults: A randomized controlled trial. Medicine (Baltimore) 2023; 102:e32951. [PMID: 36862868 PMCID: PMC9981433 DOI: 10.1097/md.0000000000032951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/04/2023] Open
Abstract
Acupoint application has served as an important complementary and adjunctive therapy in China. The purpose of this study is to explore the impact of summer acupoint application treatment (SAAT) on the abundance and biological structure of gut microbiota in healthy Asian adults. Based on the CONSORT guidelines, 72 healthy adults were included in this study, randomly divided into 2 groups, receiving either traditional (acupoint application within known relevant meridians, Group A) or sham (treated with placebo prepared by mixing the equal amount of starch and water, Group B) SAAT. SAAT stickers include extracts from Rhizoma Corydalis, Sinapis alba, Euphorbia kansui, Asari Herba, and the treatment group received 3 sessions of SAAT for 24 months, administered to BL13 (Feishu), BL17 (Geshu), BL20 (Pishu), and BL23 (Shenshu) acupoints. Fecal microbial analyses via ribosomal ribonucleic acid (rRNA) sequencing were performed on donor stool samples before and after 2 years of SAAT or placebo treatment to analyze the abundances, diversity, and structure of gut microbiota. No significant baseline differences were present between groups. At the phylum level, the baseline relative abundance of Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, and Fusobacteria was identified in fecal samples collected from each group. After treatment, the relative abundance of Firmicutes was significantly increased in both groups (P < .05). Notably, a significant decrease in the relative abundance of Fusobacteria was observed in the SAAT treatment group (P < .001), while the abundance of Bacteroidetes was decreased significantly in the placebo group (P < .05). At the genus level, the relative abundance of Faecalibacterium and Subdoligranulum species in the 2 groups were all significantly increased (P < .05). In addition, a significant reduction in the relative abundance of Blautia, Bacteroides, and Dorea in Group A (P < .05) and Eubacterium hallii group and Anaerostipes (P < .05) in Group B was observed after treatment. Our findings indicated SAAT substantially influenced the bacterial community structure in the gut microbiota of healthy Asian adults, which might serve as potential therapeutic targets for related diseases, and provided a foundation for future studies aimed at elucidating the microbial mechanisms underlying SAAT for the treatment of various conditions such as obesity, insulin resistance, irritable bowel syndrome.
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Affiliation(s)
- Jie Zhou
- Department of Project Management Division, XinDu Hospital of Traditional Chinese Medicine, Chengdu, Sichuan, PR China
| | - Bangmin Zhou
- Department of Project Management Division, XinDu Hospital of Traditional Chinese Medicine, Chengdu, Sichuan, PR China
| | - Xiaoyue Kou
- Department of Preventive Treatment, XinDu Hospital of Traditional Chinese Medicine, Chengdu, Sichuan, PR China
| | - Tao Jian
- Department of Hepatobiliary Surgery, Jintang First People’s Hospital, Chengdu, Sichuan, PR China
| | - Limei Chen
- Department of Acupuncture Rehabilitation, XinDu Hospital of Traditional Chinese Medicine, Chengdu, Sichuan, PR China
| | - Xinghua Lei
- Department of Acupuncture Rehabilitation, XinDu Hospital of Traditional Chinese Medicine, Chengdu, Sichuan, PR China
| | - Shijian Jia
- Department of Acupuncture Rehabilitation, XinDu Hospital of Traditional Chinese Medicine, Chengdu, Sichuan, PR China
| | - Xiaoying Xie
- Department of Acupuncture Rehabilitation, XinDu Hospital of Traditional Chinese Medicine, Chengdu, Sichuan, PR China
| | - Xianbo Wu
- Department of Traditional Chinese Medicine, College of Sports Medicine and Health, Chengdu Sport University, Chengdu, Sichuan, PR China
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14
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Liu J, Huang Q, Yang X, Ding C. HPE-GCN: predicting efficacy of tonic formulae via graph convolutional networks integrating traditionally defined herbal properties. Methods 2022; 204:101-109. [PMID: 35597515 DOI: 10.1016/j.ymeth.2022.05.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/04/2022] [Accepted: 05/16/2022] [Indexed: 11/29/2022] Open
Abstract
Chinese herbal formulae are the heritage of traditional Chinese medicine (TCM) in treating diseases through thousands of years. The formula function is not just a simple herbal efficacy addition, but produces complex and nonlinear relationships between different herbs and their overall efficacy, which brings challenges to the formula efficacy analysis. In our study, we proposed a model called HPE-GCN that combines graph convolutional networks (GCN) with TCM-defined herbal properties (TCM-HPs) to predict formulae efficacy. In addition, to process the unstructured natural language in the formula text, we proposed a weighting calculation method related to herb frequency and the number of herbs in a formula called Formula-Herb dependence degree (FHDD), to assess the dependency degree of a formula with its herbs. In our research, 214 classic tonic formulae from ancient TCM books such as Synopsis of the Golden Chamber, Jingyue's Complete Works and the Golden Mirror of Medicin were collected as datasets. The performance of HPE-GCN on multi-classification of tonic formulae reached the best result compared with classic machine learning models, such as support vector machine, naive Bayes, logistic regression, gradient boosting decision tree, and K-nearest neighbors. The evaluated index Macro-Precision, Macro-Recall, Macro-F1 of HPE-GCN on the test set were 87.70%, 84.08% and 83.51% respectively, increased by 7.27%, 7.41% and 7.30% respectively from second best compared models. GCN has the advantage of low-dimensional feature expression for herbs and formulae, and is an effective analysis tool for TCM research. HPE-GCN integrates TCM-HPs and fits the complex nonlinear mapping relationship between TCM-HPs and formulae efficacy, which provides new ideas for related research.
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Affiliation(s)
- Jiajun Liu
- School of Informatics, Hunan University of Chinese Medicine, Changsha, Hunan, 410208, China
| | - Qunfu Huang
- School of Informatics, Hunan University of Chinese Medicine, Changsha, Hunan, 410208, China
| | - Xiaoyan Yang
- School of Informatics, Hunan University of Chinese Medicine, Changsha, Hunan, 410208, China
| | - Changsong Ding
- School of Informatics, Hunan University of Chinese Medicine, Changsha, Hunan, 410208, China; Big Data Analysis Laboratory of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, 410208, China.
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15
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Tao Xue H, Stanley-Baker M, Wai Kin Kong A, Leung Li H, Wen Bin Goh W. Data considerations for predictive modeling applied to the discovery of bioactive natural products. Drug Discov Today 2022; 27:2235-2243. [DOI: 10.1016/j.drudis.2022.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/21/2022] [Accepted: 05/10/2022] [Indexed: 11/29/2022]
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16
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Yang F, Zhang Q, Ji X, Zhang Y, Li W, Peng S, Xue F. Machine Learning Applications in Drug Repurposing. Interdiscip Sci 2022; 14:15-21. [PMID: 35066811 PMCID: PMC8783773 DOI: 10.1007/s12539-021-00487-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 09/23/2021] [Accepted: 10/13/2021] [Indexed: 11/25/2022]
Abstract
The coronavirus disease (COVID-19) has led to an rush to repurpose existing drugs, although the underlying evidence base is of variable quality. Drug repurposing is a technique by taking advantage of existing known drugs or drug combinations to be explored in an unexpected medical scenario. Drug repurposing, hence, plays a vital role in accelerating the pre-clinical process of designing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repurposing depends on massive observed data from existing drugs and diseases, the tremendous growth of publicly available large-scale machine learning methods supplies the state-of-the-art application of data science to signaling disease, medicine, therapeutics, and identifying targets with the least error. In this article, we introduce guidelines on strategies and options of utilizing machine learning approaches for accelerating drug repurposing. We discuss how to employ machine learning methods in studying precision medicine, and as an instance, how machine learning approaches can accelerate COVID-19 drug repurposing by developing Chinese traditional medicine therapy. This article provides a strong reasonableness for employing machine learning methods for drug repurposing, including during fighting for COVID-19 pandemic.
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Affiliation(s)
- Fan Yang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012 China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012 China
| | - Qi Zhang
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012 China
| | - Xiaokang Ji
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012 China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012 China
| | - Yanchun Zhang
- Institute for Sustainable Industries & Liveable Citie, Victoria University, Melbourne, Australia
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang, 325001 China
| | - Wentao Li
- School of Computer Science, National University of Defense Technology, Changsha, China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
- School of Computer Science, National University of Defense Technology, Changsha, China
- Peng Cheng Lab, Shenzhen, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012 China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012 China
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17
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Wang S, Hou Y, Li X, Meng X, Zhang Y, Wang X. Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis. Front Pharmacol 2022; 12:765435. [PMID: 35002704 PMCID: PMC8733656 DOI: 10.3389/fphar.2021.765435] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 12/09/2021] [Indexed: 12/23/2022] Open
Abstract
Rheumatoid arthritis (RA), an autoimmune disease of unknown etiology, is a serious threat to the health of middle-aged and elderly people. Although western medicine, traditional medicine such as traditional Chinese medicine, Tibetan medicine and other ethnic medicine have shown certain advantages in the diagnosis and treatment of RA, there are still some practical shortcomings, such as delayed diagnosis, improper treatment scheme and unclear drug mechanism. At present, the applications of artificial intelligence (AI)-based deep learning and cloud computing has aroused wide attention in the medical and health field, especially in screening potential active ingredients, targets and action pathways of single drugs or prescriptions in traditional medicine and optimizing disease diagnosis and treatment models. Integrated information and analysis of RA patients based on AI and medical big data will unquestionably benefit more RA patients worldwide. In this review, we mainly elaborated the application status and prospect of AI-assisted deep learning and cloud computation-oriented western medicine and traditional medicine on the diagnosis and treatment of RA in different stages. It can be predicted that with the help of AI, more pharmacological mechanisms of effective ethnic drugs against RA will be elucidated and more accurate solutions will be provided for the treatment and diagnosis of RA in the future.
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Affiliation(s)
- Shaohui Wang
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Ya Hou
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Xuanhao Li
- Chengdu Second People's Hospital, Chengdu, China
| | - Xianli Meng
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yi Zhang
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Xiaobo Wang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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18
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Chen J, Yang W, Tan G, Tian C, Wang H, Zhou J, Liao H. Prediction of the taxonomical classification of the Ranunculaceae family using a machine learning method. NEW J CHEM 2022. [DOI: 10.1039/d1nj03632g] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
A machine learning method is successfully applied to determine lineage-specific features among various genera within the Ranunculaceae family.
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Affiliation(s)
- Jiao Chen
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
| | - Wenlu Yang
- Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
| | - Guodong Tan
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
| | - Chunyao Tian
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
| | - Hongjun Wang
- Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
| | - Jiayu Zhou
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
| | - Hai Liao
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
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19
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Li JT, Wei YW, Wang MY, Yan CX, Ren X, Fu XJ. Antibacterial Activity Prediction Model of Traditional Chinese Medicine Based on Combined Data-Driven Approach and Machine Learning Algorithm: Constructed and Validated. Front Microbiol 2021; 12:763498. [PMID: 34880839 PMCID: PMC8645695 DOI: 10.3389/fmicb.2021.763498] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 10/27/2021] [Indexed: 11/13/2022] Open
Abstract
Traditional Chinese medicines (TCMs), as a unique natural medicine resource, were used to prevent and treat bacterial diseases in China with a long history. To provide a prediction model of screening antibacterial TCMs for the design and discovery of novel antibacterial agents, the literature about antibacterial TCMs in the China National Knowledge Infrastructure (CNKI) and Web of Science database was retrieved. The data were extracted and standardized. A total of 28,786 pieces of data from 904 antibacterial TCMs were collected. The data of plant medicine were the most numerous. The result of association rules mining showed a high correlation between antibacterial activity with cold nature, bitter and sour tastes, hemostatic, and purging fire efficacies. Moreover, TCMs with antibacterial activity showed a specific aggregation in the phylogenetic tree; 92% of them came from Tracheophyta, of which 74% were mainly concentrated in rosids, asterids, Liliopsida, and Ranunculales. The prediction models of anti-Escherichia coli and anti-Staphylococcus aureus activity, with AUC values (the area under the ROC curve) of 77.5 and 80.0%, respectively, were constructed by the Neural Networks (NN) algorithm after Bagged Classification and Regression Tree (Bagged CART) and Linear Discriminant Analysis (LDA) selection. The in vitro experimental results showed the prediction accuracy of these two models was 75 and 60%, respectively. Four TCMs (Cirsii Japonici Herba Carbonisata, Changii Radix, Swertiae Herba, Callicarpae Formosanae Folium) were proposed for the first time to show antibacterial activity against E. coli and/or S. aureus. The results implied that the prediction model of antibacterial activity of TCMs based on properties and families showed certain prediction ability, which was of great significance to the screening of antibacterial TCMs and can be used to discover novel antibacterial agents.
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Affiliation(s)
- Jin-Tong Li
- Institute of Traditional Chinese Medicine Literature and Culture, Shandong University of Traditional Chinese Medicine, Jinan, China.,Marine Traditional Chinese Medicine Research Center, Shandong University of Traditional Chinese Medicine, Qingdao Academy of Traditional Chinese Medical Science, Qingdao, China
| | - Ya-Wen Wei
- Marine Traditional Chinese Medicine Research Center, Shandong University of Traditional Chinese Medicine, Qingdao Academy of Traditional Chinese Medical Science, Qingdao, China.,College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Meng-Yu Wang
- Institute of Traditional Chinese Medicine Literature and Culture, Shandong University of Traditional Chinese Medicine, Jinan, China.,Marine Traditional Chinese Medicine Research Center, Shandong University of Traditional Chinese Medicine, Qingdao Academy of Traditional Chinese Medical Science, Qingdao, China
| | - Chun-Xiao Yan
- Marine Traditional Chinese Medicine Research Center, Shandong University of Traditional Chinese Medicine, Qingdao Academy of Traditional Chinese Medical Science, Qingdao, China
| | - Xia Ren
- Marine Traditional Chinese Medicine Research Center, Shandong University of Traditional Chinese Medicine, Qingdao Academy of Traditional Chinese Medical Science, Qingdao, China.,Shandong Engineering and Technology Research Center of Traditional Chinese Medicine, Jinan, China
| | - Xian-Jun Fu
- Institute of Traditional Chinese Medicine Literature and Culture, Shandong University of Traditional Chinese Medicine, Jinan, China.,Marine Traditional Chinese Medicine Research Center, Shandong University of Traditional Chinese Medicine, Qingdao Academy of Traditional Chinese Medical Science, Qingdao, China.,Shandong Engineering and Technology Research Center of Traditional Chinese Medicine, Jinan, China
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20
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Gong X, Yuan B, Yuan Y, Li F. Efficacy and Safety of Lianhuaqingwen Capsules for the Prevention of Coronavirus Disease 2019: A Prospective Open-Label Controlled Trial. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2021; 2021:7962630. [PMID: 34858512 PMCID: PMC8632393 DOI: 10.1155/2021/7962630] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 10/30/2021] [Indexed: 12/23/2022]
Abstract
Coronavirus disease 2019 (COVID-19) has become a global pandemic. Community and close contact exposures continue to drive the COVID-19 pandemic. There is no confirmed effective treatment for suspected cases and close contacts. Lianhuaqingwen (LH) capsules, a repurposed Chinese herbal product that is currently on the market, have proven effective for influenza and COVID-19. To determine the safety and efficacy of LH capsules for the prevention of COVID-19, we conducted a prospective open-label controlled trial of LH capsules on subjects who had close contact with people infected with COVID-19. Subjects received LH capsules (4 capsules, three times daily) or the usual medical observation for 14 days. The primary endpoint was the rate of positive nucleic acid tests of nasal and pharyngeal swabs during the quarantine medical observation period. We included 1976 patients, including 1101 in the treatment group and 875 in the control group. The rate of positive nucleic acid tests in the treatment group was significantly lower than that in the control group (0.27% vs. 1.14%, respectively; mean difference: -0.87%; 95% CI: -1.83 to -0.13; p=0.0174) during the quarantine medical observation period (14 days). Among subjects with different close contact states, there was no significant difference in the rate of positive nucleic acid test results among close contacts in the treatment group and the control group (6.45% vs. 11.43%, respectively; p=0.6762). Among secondary close contacts, the rate of positive nucleic acid tests in the treatment group was significantly lower than that in the control group (0.09% vs. 0.71%, respectively; p=0.0485). No serious adverse events were reported. Taken together, and in light of the safety and effectiveness profiles, these results show that LH capsules can be considered to prevent the progression of COVID-19 after close contact with an infected person. This trial is registered with ChiCTR2100043012.
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Affiliation(s)
- Xiaowei Gong
- Department of Respiratory and Critical Care Medicine, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, China
| | - Boyun Yuan
- Department of Respiratory and Critical Care Medicine, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, China
| | - Yadong Yuan
- Department of Respiratory and Critical Care Medicine, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, China
| | - Fengju Li
- Department of Medical Imaging, Hebei Provincial Corps Hospital of CPAPF, Shijiazhuang, Hebei Province, China
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21
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Jeon J, Kang S, Kim HU. Predicting biochemical and physiological effects of natural products from molecular structures using machine learning. Nat Prod Rep 2021; 38:1954-1966. [PMID: 34047331 DOI: 10.1039/d1np00016k] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Covering: 2016 to 2021Discovery of novel natural products has been greatly facilitated by advances in genome sequencing, genome mining and analytical techniques. As a result, the volume of data for natural products has increased over the years, which started to serve as ingredients for developing machine learning models. In the past few years, a number of machine learning models have been developed to examine various aspects of a molecule by effectively processing its molecular structure. Understanding of the biological effects of natural products can benefit from such machine learning approaches. In this context, this Highlight reviews recent studies on machine learning models developed to infer various biological effects of molecules. A particular attention is paid to molecular featurization, or computational representation of a molecular structure, which is an essential process during the development of a machine learning model. Technical challenges associated with the use of machine learning for natural products are further discussed.
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Affiliation(s)
- Junhyeok Jeon
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Seongmo Kang
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea. and KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, Republic of Korea and BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea
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22
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Singh H, Bharadvaja N. Treasuring the computational approach in medicinal plant research. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2021; 164:19-32. [PMID: 34004233 DOI: 10.1016/j.pbiomolbio.2021.05.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 05/11/2021] [Indexed: 01/24/2023]
Abstract
Medicinal plants serve as a valuable source of secondary metabolites since time immemorial. Computational Research in 21st century is giving more attention to medicinal plants for new drug design as pharmacological screening of bioactive compound was time consuming and expensive. Computational methods such as Molecular Docking, Molecular Dynamic Simulation and Artificial intelligence are significant Insilico tools in medicinal plant research. Molecular docking approach exploits the mechanism of potential phytochemicals into the target active site to elucidate its interactions and biological therapeutic properties. MD simulation illuminates the dynamic behavior of biomolecules at atomic level with fine quality representation of biomolecules. Dramatical advancement in computer science is illustrating the biological mechanism via these tools in different diseases treatment. The advancement comprises speed, the system configuration, and other software upgradation to insights into the structural explanation and optimization of biomolecules. A probable shift from simulation to artificial intelligence has in fact accelerated the art of scientific study to a sky high. The most upgraded algorithm in artificial intelligence such as Artificial Neural Networks, Deep Neural Networks, Neuro-fuzzy Logic has provided a wide opportunity in easing the time required in classical experimental strategy. The notable progress in computer science technology has paved a pathway for understanding the pharmacological functions and creating a roadmap for drug design and development and other achievement in the field of medicinal plants research. This review focus on the development and overview in computational research moving from static molecular docking method to a range of dynamic simulation and an advanced artificial intelligence such as machine learning.
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Affiliation(s)
- Harshita Singh
- Plant Biotechnology Laboratory, Delhi Technological University, Delhi, 110042, India
| | - Navneeta Bharadvaja
- Plant Biotechnology Laboratory, Delhi Technological University, Delhi, 110042, India.
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23
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Santana K, do Nascimento LD, Lima e Lima A, Damasceno V, Nahum C, Braga RC, Lameira J. Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products. Front Chem 2021; 9:662688. [PMID: 33996755 PMCID: PMC8117418 DOI: 10.3389/fchem.2021.662688] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 02/25/2021] [Indexed: 12/22/2022] Open
Abstract
Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.
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Affiliation(s)
- Kauê Santana
- Instituto de Biodiversidade, Universidade Federal do Oeste do Pará, Santarém, Brazil
| | | | - Anderson Lima e Lima
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Vinícius Damasceno
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Claudio Nahum
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | | | - Jerônimo Lameira
- Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil
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Naghizadeh A, Salamat M, Hamzeian D, Akbari S, Rezaeizadeh H, Vaghasloo MA, Karbalaei R, Mirzaie M, Karimi M, Jafari M. IrGO: Iranian traditional medicine General Ontology and knowledge base. J Biomed Semantics 2021; 12:9. [PMID: 33863373 PMCID: PMC8052758 DOI: 10.1186/s13326-021-00237-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 03/04/2021] [Indexed: 11/22/2022] Open
Abstract
Background Iranian traditional medicine, also known as Persian Medicine, is a holistic school of medicine with a long prolific history. It describes numerous concepts and the relationships between them. However, no unified language system has been proposed for the concepts of this medicine up to the present time. Considering the extensive terminology in the numerous textbooks written by the scholars over centuries, comprehending the totality of concepts is obviously a very challenging task. To resolve this issue, overcome the obstacles, and code the concepts in a reusable manner, constructing an ontology of the concepts of Iranian traditional medicine seems a necessity. Construction and content Makhzan al-Advieh, an encyclopedia of materia medica compiled by Mohammad Hossein Aghili Khorasani, was selected as the resource to create an ontology of the concepts used to describe medicinal substances. The steps followed to accomplish this task included (1) compiling the list of classes via examination of textbooks, and text mining the resource followed by manual review to ensure comprehensiveness of extracted terms; (2) arranging the classes in a taxonomy; (3) determining object and data properties; (4) specifying annotation properties including ID, labels (English and Persian), alternative terms, and definitions (English and Persian); (5) ontology evaluation. The ontology was created using Protégé with adherence to the principles of ontology development provided by the Open Biological and Biomedical Ontology (OBO) foundry. Utility and discussion The ontology was finalized with inclusion of 3521 classes, 15 properties, and 20,903 axioms in the Iranian traditional medicine General Ontology (IrGO) database, freely available at http://ir-go.net/. An indented list and an interactive graph view using WebVOWL were used to visualize the ontology. All classes were linked to their instances in UNaProd database to create a knowledge base of ITM materia medica. Conclusion We constructed an ontology-based knowledge base of ITM concepts in the domain of materia medica to help offer a shared and common understanding of this concept, enable reuse of the knowledge, and make the assumptions explicit. This ontology will aid Persian medicine practitioners in clinical decision-making to select drugs. Extending IrGO will bridge the gap between traditional and conventional schools of medicine, helping guide future research in the process of drug discovery.
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Affiliation(s)
- Ayeh Naghizadeh
- Department of Traditional Medicine, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahdi Salamat
- Department of Traditional Medicine, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Donya Hamzeian
- Department of Traditional Medicine, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Shaghayegh Akbari
- Department of Traditional Medicine, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Rezaeizadeh
- Department of Traditional Medicine, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahdi Alizadeh Vaghasloo
- Department of Traditional Medicine, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Mehdi Mirzaie
- Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modarres University, Jalal Ale Ahmad Highway, Tehran, Iran
| | - Mehrdad Karimi
- Department of Traditional Medicine, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohieddin Jafari
- Department of Traditional Medicine, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran.
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Fu R, Li J, Yu H, Zhang Y, Xu Z, Martin C. The Yin and Yang of traditional Chinese and Western medicine. Med Res Rev 2021; 41:3182-3200. [PMID: 33599314 DOI: 10.1002/med.21793] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 02/03/2021] [Accepted: 02/05/2021] [Indexed: 01/22/2023]
Abstract
The success of Western Scientific approaches to medicine, over the last 150 years, can be measured by substantial increases in life expectancy, reductions in infant mortality and the virtual elimination of many infectious diseases accompanied by development of effective management practices for noncommunicable diseases. However, major challenges remain in the form of infectious diseases that evolve resistance to pharmaceuticals rapidly, new diseases, particularly those caused by viruses and effective long-term treatments for chronic, noncommunicable diseases. Traditional Chinese Medicine (TCM) can offer complementary treatments based on personalised interventions, informed by knowledge accumulated from empirical observations gathered over centuries of practice, that address the impact of disease on the whole body. We provide examples of both infectious and noncommunicable diseases where the combination of Western Scientific Medicine (WSM) and TCM can benefit patients in terms of the speed and efficacy of recovery or disease management. TCM is a healing skill based on practice, while WSM is scientific, based on experiments. Against this background, an understanding of the mechanisms of action of traditional Chinese medicinal preparations will offer fresh routes to discovery and development of new therapeutics as well as patented medical prescriptions, which will rely heavily on modern scientific methodologies for their adoption and success, particularly those in plant genomics, plant breeding and synthetic biology.
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Affiliation(s)
- Rao Fu
- Key Laboratory of Bio-resource and Eco-environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Jie Li
- Department of Metabolic Biology, John Innes Centre, Norwich Research Park, Norwich, UK
| | - Huatao Yu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yang Zhang
- Key Laboratory of Bio-resource and Eco-environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Zhihong Xu
- State Key Laboratory of Protein and Plant Gene Research, School of Life Science, Peking University, Beijing, China
| | - Cathie Martin
- Department of Metabolic Biology, John Innes Centre, Norwich Research Park, Norwich, UK
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Tang Y, Li Z, Yang D, Fang Y, Gao S, Liang S, Liu T. Research of insomnia on traditional Chinese medicine diagnosis and treatment based on machine learning. Chin Med 2021; 16:2. [PMID: 33407711 PMCID: PMC7789502 DOI: 10.1186/s13020-020-00409-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 12/10/2020] [Indexed: 11/10/2022] Open
Abstract
Background Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. Methods First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest. Results Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinations of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment. Conclusions The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment.
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Affiliation(s)
- Yuqi Tang
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, China
| | - Zechen Li
- School of Automation, Chongqing University, Chongqing, 400044, China
| | - Dongdong Yang
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, China.
| | - Yu Fang
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, China
| | - Shanshan Gao
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, China
| | - Shan Liang
- School of Automation, Chongqing University, Chongqing, 400044, China
| | - Tao Liu
- Electronic Engineering College, Chengdu University of Information Technology, Chengdu, 610225, China
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Zhang R, Li X, Zhang X, Qin H, Xiao W. Machine learning approaches for elucidating the biological effects of natural products. Nat Prod Rep 2021; 38:346-361. [PMID: 32869826 DOI: 10.1039/d0np00043d] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural products (NPs) has developed in order to manage the challenge of the discovery of bioactive NPs. In the present review, we will introduce the basic principles and protocols for using the ML approach to investigate the bioactivity of NPs, citing a series of practical examples regarding the study of anti-microbial, anti-cancer, and anti-inflammatory NPs, etc. ML algorithms manage a variety of classification and regression problems associated with bioactive NPs, from those that are linear to non-linear and from pure compounds to plant extracts. Inspired by cases reported in the literature and our own experience, a number of key points have been emphasized for reducing modeling errors, including dataset preparation and applicability domain analysis.
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Affiliation(s)
- Ruihan Zhang
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Xiaoli Li
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Xingjie Zhang
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Huayan Qin
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Weilie Xiao
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
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Bu H, Li X, Hu L, Wang J, Li Y, Zhao T, Wang H, Wang S. The anti-inflammatory mechanism of the medicinal fungus puffball analysis based on network pharmacology. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
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Yu YD, Xiu YP, Li YF, Xue YT. To Explore the Mechanism and Equivalent Molecular Group of Fuxin Mixture in Treating Heart Failure Based on Network Pharmacology. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2020; 2020:8852877. [PMID: 33273955 PMCID: PMC7700035 DOI: 10.1155/2020/8852877] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 10/25/2020] [Accepted: 11/11/2020] [Indexed: 12/27/2022]
Abstract
Fuxin mixture (FXHJ) is a prescription for the treatment of heart failure. It has been shown to be effective in clinical trials, but its active ingredients and mechanism of action are not completely clear, which limits its clinical application and international promotion. In this study, we used network pharmacology to find, conclude, and summarize the mechanism of FXHJ in the treatment of heart failure. From FXHJ, we found 39 active ingredients and 47 action targets. Next, we constructed the action network and was conducted enrichment analysis. The results showed that FXHJ mainly treated heart failure by regulating the MAPK signaling pathway, PI3KAkt signaling pathway, cAMP signaling pathway, TNF signaling pathway, toll-like receptor signaling pathway, VEGF signaling pathway, NF-kappa B signaling pathway, and the apoptotic signaling molecule BCL2. Through the research method of network pharmacology, this study summarized the preliminary experiments of the research group and revealed the probable mechanism of FXHJ in the treatment of heart failure to a certain extent, which provided some ideas for the development of new drugs.
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Affiliation(s)
- Yi-ding Yu
- Shandong University of Traditional Chinese Medicine, Jinan 250014, China
| | - Yi-ping Xiu
- Shandong University of Traditional Chinese Medicine, Jinan 250014, China
| | - Yang-fan Li
- Shandong University of Traditional Chinese Medicine, Jinan 250014, China
| | - Yi-tao Xue
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250014, China
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30
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Capecchi A, Reymond JL. Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning. Biomolecules 2020; 10:E1385. [PMID: 32998475 PMCID: PMC7600738 DOI: 10.3390/biom10101385] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 09/22/2020] [Accepted: 09/25/2020] [Indexed: 12/20/2022] Open
Abstract
Microbial natural products (NPs) are an important source of drugs, however, their structural diversity remains poorly understood. Here we used our recently reported MinHashed Atom Pair fingerprint with diameter of four bonds (MAP4), a fingerprint suitable for molecules across very different sizes, to analyze the Natural Products Atlas (NPAtlas), a database of 25,523 NPs of bacterial or fungal origin. To visualize NPAtlas by MAP4 similarity, we used the dimensionality reduction method tree map (TMAP). The resulting interactive map organizes molecules by physico-chemical properties and compound families such as peptides and glycosides. Remarkably, the map separates bacterial and fungal NPs from one another, revealing that these two compound families are intrinsically different despite their related biosynthetic pathways. We used these differences to train a machine learning model capable of distinguishing between NPs of bacterial or fungal origin.
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Affiliation(s)
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland;
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31
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Jafari M, Wang Y, Amiryousefi A, Tang J. Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine. Front Pharmacol 2020; 11:1319. [PMID: 32982738 PMCID: PMC7479204 DOI: 10.3389/fphar.2020.01319] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 08/07/2020] [Indexed: 12/11/2022] Open
Abstract
The ultimate goal of precision medicine is to determine right treatment for right patients based on precise diagnosis. To achieve this goal, correct stratification of patients using molecular features and clinical phenotypes is crucial. During the long history of medical science, our understanding on disease classification has been improved greatly by chemistry and molecular biology. Nowadays, we gain access to large scale patient-derived data by high-throughput technologies, generating a greater need for data science including unsupervised learning and network modeling. Unsupervised learning methods such as clustering could be a better solution to stratify patients when there is a lack of predefined classifiers. In network modularity analysis, clustering methods can be also applied to elucidate the complex structure of biological and disease networks at the systems level. In this review, we went over the main points of clustering analysis and network modeling, particularly in the context of Traditional Chinese medicine (TCM). We showed that this approach can provide novel insights on the rationale of classification for TCM herbs. In a case study, using a modularity analysis of multipartite networks, we illustrated that the TCM classifications are associated with the chemical properties of the herb ingredients. We concluded that multipartite network modeling may become a suitable data integration tool for understanding the mechanisms of actions of traditional medicine.
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Affiliation(s)
- Mohieddin Jafari
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Yinyin Wang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ali Amiryousefi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
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32
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Yeh HY, Chao CT, Lai YP, Chen HW. Predicting the Associations between Meridians and Chinese Traditional Medicine Using a Cost-Sensitive Graph Convolutional Neural Network. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17030740. [PMID: 31979314 PMCID: PMC7036907 DOI: 10.3390/ijerph17030740] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 01/09/2020] [Accepted: 01/16/2020] [Indexed: 12/17/2022]
Abstract
Natural products are the most important and commonly used in Traditional Chinese Medicine (TCM) for healthcare and disease prevention in East-Asia. Although the Meridian system of TCM was established several thousand years ago, the rationale of Meridian classification based on the ingredient compounds remains poorly understood. A core challenge for the traditional machine learning approaches for chemical activity prediction is to encode molecules into fixed length vectors but ignore the structural information of the chemical compound. Therefore, we apply a cost-sensitive graph convolutional neural network model to learn local and global topological features of chemical compounds, and discover the associations between TCM and their Meridians. In the experiments, we find that the performance of our approach with the area under the receiver operating characteristic curve (ROC-AUC) of 0.82 which is better than the traditional machine learning algorithm and also obtains 8%–13% improvement comparing with the state-of-the-art methods. We investigate the powerful ability of deep learning approach to learn the proper molecular descriptors for Meridian prediction and to provide novel insights into the complementary and alternative medicine of TCM.
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Affiliation(s)
- Hsiang-Yuan Yeh
- School of Big Data Management, Soochow University, Taipei 111, Taiwan
- Correspondence:
| | - Chia-Ter Chao
- Department of Medicine, National Taiwan University Hospital BeiHu Branch, College of Medicine, National Taiwan University, Taipei 10617, Taiwan
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei 10617, Taiwan
- Graduate Institute of Toxicology, College of Medicine, National Taiwan University, Taipei 10617, Taiwan
| | - Yi-Pei Lai
- School of Big Data Management, Soochow University, Taipei 111, Taiwan
| | - Huei-Wen Chen
- Graduate Institute of Toxicology, College of Medicine, National Taiwan University, Taipei 10617, Taiwan
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Hu S, Chen S, Li Z, Wang Y, Wang Y. Research on the potential mechanism of Chuanxiong Rhizoma on treating Diabetic Nephropathy based on network pharmacology. Int J Med Sci 2020; 17:2240-2247. [PMID: 32922187 PMCID: PMC7484651 DOI: 10.7150/ijms.47555] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 08/12/2020] [Indexed: 12/14/2022] Open
Abstract
Background: Chuanxiong Rhizoma is one of the traditional Chinese medicines which have been used for years in the treatment of diabetic nephropathy (DN). However, the mechanism of Chuanxiong Rhizoma in DN has not yet been fully understood. Methods: We performed network pharmacology to construct target proteins interaction network of Chuanxiong Rhizoma. Active ingredients were acquired from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. DRUGBANK database was used to predict target proteins of Chuanxiong Rhizoma. Gene ontology (GO) biological process analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were also performed for functional prediction of the target proteins. Molecular docking was applied for evaluating the drug interactions between hub targets and active ingredients. Results: Twenty-eight target genes fished by 6 active ingredients of Chuanxiong Rhizoma were obtained in the study. The top 10 significant GO analyses and 6 KEGG pathways were enriched for genomic analysis. We also acquired 1366 differentially expressed genes associated with DN from GSE30528 dataset, including five target genes: KCNH2, NCOA1, KDR, NR3C2 and ADRB2. Molecular docking analysis successfully combined KCNH2, NCOA1, KDR and ADRB2 to Myricanone with docking scores from 4.61 to 6.28. NR3C2 also displayed good docking scores with Wallichilide and Sitosterol (8.13 and 8.34, respectively), revealing good binding forces to active compounds of Chuanxiong Rhizoma. Conclusions: Chuanxiong Rhizoma might take part in the treatment of DN through pathways associated with steroid hormone, estrogen, thyroid hormone and IL-17. KCNH2, NCOA1, KDR, ADRB2 and NR3C2 were proved to be the hub targets, which were closely related to corresponding active ingredients of Chuanxiong Rhizoma.
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Affiliation(s)
- Shanshan Hu
- Department of Pharmacy, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China
| | - Siteng Chen
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Zhilei Li
- Department of Pharmacy, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China
| | - Yuhang Wang
- Department of Pharmacy, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China
| | - Yong Wang
- Department of Pharmacy, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China.,Laboratory of Research of New Chinese Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China
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Khan T, Ali M, Khan A, Nisar P, Jan SA, Afridi S, Shinwari ZK. Anticancer Plants: A Review of the Active Phytochemicals, Applications in Animal Models, and Regulatory Aspects. Biomolecules 2019; 10:E47. [PMID: 31892257 PMCID: PMC7022400 DOI: 10.3390/biom10010047] [Citation(s) in RCA: 160] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 12/24/2019] [Accepted: 12/25/2019] [Indexed: 12/24/2022] Open
Abstract
The rising burden of cancer worldwide calls for an alternative treatment solution. Herbal medicine provides a very feasible alternative to western medicine against cancer. This article reviews the selected plant species with active phytochemicals, the animal models used for these studies, and their regulatory aspects. This study is based on a meticulous literature review conducted through the search of relevant keywords in databases, Web of Science, Scopus, PubMed, and Google Scholar. Twenty plants were selected based on defined selection criteria for their potent anticancer compounds. The detailed analysis of the research studies revealed that plants play an indispensable role in fighting different cancers such as breast, stomach, oral, colon, lung, hepatic, cervical, and blood cancer cell lines. The in vitro studies showed cancer cell inhibition through DNA damage and activation of apoptosis-inducing enzymes by the secondary metabolites in the plant extracts. Studies that reported in vivo activities of these plants showed remarkable results in the inhibition of cancer in animal models. Further studies should be performed on exploring more plants, their active compounds, and the mechanism of anticancer actions for use as standard herbal medicine.
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Affiliation(s)
- Tariq Khan
- Department of Biotechnology, University of Malakand, Chakdara 18800, Pakistan
| | - Muhammad Ali
- Department of Biotechnology, Quaid-i-Azam University, Islamabad 45320, Pakistan; (P.N.); (S.A.); (Z.K.S.)
| | - Ajmal Khan
- Department of Zoology, University of Buner, Sowari 17290, Pakistan;
| | - Parveen Nisar
- Department of Biotechnology, Quaid-i-Azam University, Islamabad 45320, Pakistan; (P.N.); (S.A.); (Z.K.S.)
| | - Sohail Ahmad Jan
- Department of Biotechnology, Hazara University, Mansehra 21120, Pakistan;
| | - Shakeeb Afridi
- Department of Biotechnology, Quaid-i-Azam University, Islamabad 45320, Pakistan; (P.N.); (S.A.); (Z.K.S.)
| | - Zabta Khan Shinwari
- Department of Biotechnology, Quaid-i-Azam University, Islamabad 45320, Pakistan; (P.N.); (S.A.); (Z.K.S.)
- National Council for Tibb, Islamabad, Pakistan
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