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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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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: 2.0] [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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Khan W, Zaki N, Ahmad A, Bian J, Ali L, Mehedy Masud M, Ghenimi N, Ahmed LA. Infant Low Birth Weight Prediction Using Graph Embedding Features. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1317. [PMID: 36674072 PMCID: PMC9859143 DOI: 10.3390/ijerph20021317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/30/2022] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
Low Birth weight (LBW) infants pose a serious public health concern worldwide in both the short and long term for infants and their mothers. Infant weight prediction prior to birth can help to identify risk factors and reduce the risk of infant morbidity and mortality. Although many Machine Learning (ML) algorithms have been proposed for LBW prediction using maternal features and produced considerable model performance, their performance needs to be improved so that they can be adapted in real-world clinical settings. Existing algorithms used for LBW classification often fail to capture structural information from the tabular dataset of patients with different complications. Therefore, to improve the LBW classification performance, we propose a solution by transforming the tabular data into a knowledge graph with the aim that patients from the same class (normal or LBW) exhibit similar patterns in the graphs. To achieve this, several features related to each node are extracted such as node embedding using node2vec algorithm, node degree, node similarity, nearest neighbors, etc. Our method is evaluated on a real-life dataset obtained from a large cohort study in the United Arab Emirates which contains data from 3453 patients. Multiple experiments were performed using the seven most commonly used ML models on the original dataset, graph features, and a combination of features, respectively. Experimental results show that our proposed method achieved the best performance with an area under the curve of 0.834 which is over 6% improvement compared to using the original risk factors without transforming them into knowledge graphs. Furthermore, we provide the clinical relevance of the proposed model that are important for the model to be adapted in clinical settings.
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Affiliation(s)
- Wasif Khan
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Nazar Zaki
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Amir Ahmad
- Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, USA
| | - Luqman Ali
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Mohammad Mehedy Masud
- Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Nadirah Ghenimi
- Department Family Medicine, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Luai A. Ahmed
- Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
- Zayed Centre for Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
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Chu H, Moon S, Park J, Bak S, Ko Y, Youn BY. The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review. Front Pharmacol 2022; 13:826044. [PMID: 35431917 PMCID: PMC9011141 DOI: 10.3389/fphar.2022.826044] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/01/2022] [Indexed: 01/04/2023] Open
Abstract
Background: The development of artificial intelligence (AI) in the medical field has been growing rapidly. As AI models have been introduced in complementary and alternative medicine (CAM), a systematized review must be performed to understand its current status. Objective: To categorize and seek the current usage of AI in CAM. Method: A systematic scoping review was conducted based on the method proposed by the Joanna Briggs Institute. The three databases, PubMed, Embase, and Cochrane Library, were used to find studies regarding AI and CAM. Only English studies from 2000 were included. Studies without mentioning either AI techniques or CAM modalities were excluded along with the non-peer-reviewed studies. A broad-range search strategy was applied to locate all relevant studies. Results: A total of 32 studies were identified, and three main categories were revealed: 1) acupuncture treatment, 2) tongue and lip diagnoses, and 3) herbal medicine. Other CAM modalities were music therapy, meditation, pulse diagnosis, and TCM syndromes. The majority of the studies utilized AI models to predict certain patterns and find reliable computerized models to assist physicians. Conclusion: Although the results from this review have shown the potential use of AI models in CAM, future research ought to focus on verifying and validating the models by performing a large-scale clinical trial to better promote AI in CAM in the era of digital health.
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Affiliation(s)
- Hongmin Chu
- Daecheong Public Health Subcenter, Incheon, South Korea
| | - Seunghwan Moon
- Department of Global Public Health and Korean Medicine Management, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Jeongsu Park
- Department of College of Korean Medicine, Wonkwang University, Iksan, South Korea
| | - Seongjun Bak
- Department of College of Korean Medicine, Wonkwang University, Iksan, South Korea
| | - Youme Ko
- National Institute for Korean Medicine Development (NIKOM), Seoul, South Korea
| | - Bo-Young Youn
- Department of Preventive Medicine, College of Korean Medicine, Kyung Hee University, Seoul, South Korea
- *Correspondence: Bo-Young Youn,
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Cheng N, Chen Y, Gao W, Liu J, Huang Q, Yan C, Huang X, Ding C. An Improved Deep Learning Model: S-TextBLCNN for Traditional Chinese Medicine Formula Classification. Front Genet 2022; 12:807825. [PMID: 35003231 PMCID: PMC8727750 DOI: 10.3389/fgene.2021.807825] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 11/15/2021] [Indexed: 11/18/2022] Open
Abstract
Purpose: This study proposes an S-TextBLCNN model for the efficacy of traditional Chinese medicine (TCM) formula classification. This model uses deep learning to analyze the relationship between herb efficacy and formula efficacy, which is helpful in further exploring the internal rules of formula combination. Methods: First, for the TCM herbs extracted from Chinese Pharmacopoeia, natural language processing (NLP) is used to learn and realize the quantitative expression of different TCM herbs. Three features of herb name, herb properties, and herb efficacy are selected to encode herbs and to construct formula-vector and herb-vector. Then, based on 2,664 formulae for stroke collected in TCM literature and 19 formula efficacy categories extracted from Yifang Jijie, an improved deep learning model TextBLCNN consists of a bidirectional long short-term memory (Bi-LSTM) neural network and a convolutional neural network (CNN) is proposed. Based on 19 formula efficacy categories, binary classifiers are established to classify the TCM formulae. Finally, aiming at the imbalance problem of formula data, the over-sampling method SMOTE is used to solve it and the S-TextBLCNN model is proposed. Results: The formula-vector composed of herb efficacy has the best effect on the classification model, so it can be inferred that there is a strong relationship between herb efficacy and formula efficacy. The TextBLCNN model has an accuracy of 0.858 and an F1-score of 0.762, both higher than the logistic regression (acc = 0.561, F1-score = 0.567), SVM (acc = 0.703, F1-score = 0.591), LSTM (acc = 0.723, F1-score = 0.621), and TextCNN (acc = 0.745, F1-score = 0.644) models. In addition, the over-sampling method SMOTE is used in our model to tackle data imbalance, and the F1-score is greatly improved by an average of 47.1% in 19 models. Conclusion: The combination of formula feature representation and the S-TextBLCNN model improve the accuracy in formula efficacy classification. It provides a new research idea for the study of TCM formula compatibility.
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Affiliation(s)
- Ning Cheng
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
| | - Yue Chen
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
| | - Wanqing Gao
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
| | - Jiajun Liu
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
| | - Qunfu Huang
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
| | - Cheng Yan
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China.,Big Data Analysis Laboratory of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Xindi Huang
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
| | - Changsong Ding
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China.,Big Data Analysis Laboratory of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, China
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Shu Z, Jia T, Tian H, Yan D, Yang Y, Zhou X. AIM in Alternative Medicine. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_57] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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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: 3.7] [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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AIM in Alternative Medicine. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_57-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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