Deep learning and machine intelligence: New computational modeling techniques for discovery of the combination rules and pharmacodynamic characteristics of Traditional Chinese Medicine

Li, Dongna; Hu, Jing; Zhang, Lin; Li, Lili; Yin, Qingsheng; Shi, Jiangwei; Guo, Hong; Zhang, Yanjun; Zhuang, Pengwei

doi:10.1016/j.ejphar.2022.175260

Cited by 21 publications

(14 citation statements)

References 87 publications

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“…However, DTs are unstable since small variations in the data may result in the generation of a completely different tree. On the other hand, although RF shows good capability to reduce data noise, it is prone to overfitting when training large amounts of data [ 45 ]. RF has a simple structure, and is ease of understanding, performs higher efficiency than similar methods [ 37 ] (Table 1 ).…”

Section: Machine Learning Algorithms-traditional Machine Learning Alg...mentioning

confidence: 99%

“…Approximately 70-95% of people in the developing world continue to rely on natural products as their primary pharmacopeia [47]. Thus, the development of natural products is of a great importance in clinical therapy, especially in combination with machine learning, is an innovative, forward-looking, and applicable RF Simple structure; Easy to implement; Higher efficiency [37] Unable to optimize its own parameters; Overfitting can easily occur when the amount of data is large [45] Classification and regression problems [44] Table 2 Applications of machine learning in TCM research…”

Section: Applications Of Machine Learning In Natural Products Develop...mentioning

confidence: 99%

“…Tongue diagnosis provides much information about the state of the body, and the diagnostic points of tongue diagnosis include the color and state of the tongue coating, color, texture, shape, and characteristics of the sublingual vein and tongue body parts, etc., which are important features in tongue diagnosis data collection. Traditional machine learning algorithms such as SVM [66], DT, neural networks [45], and RF [67] have been previously used as intelligent auxiliary diagnosis algorithms for tongue diagnosis. The accuracy of SVM in processing hyperspectral red-green-blue tongue images based on tissue type combination can be as high as 93.11% [66].…”

Section: Applications Of Machine Learning In Disease Diagnosismentioning

confidence: 99%

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Machine learning in TCM with natural products and molecules: current status and future perspectives

Liu

et al. 2023

Chin Med

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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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Section: Machine Learning Algorithms-traditional Machine Learning Alg...mentioning

confidence: 99%

Section: Applications Of Machine Learning In Natural Products Develop...mentioning

confidence: 99%

Section: Applications Of Machine Learning In Disease Diagnosismentioning

confidence: 99%

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Machine learning in TCM with natural products and molecules: current status and future perspectives

Liu

et al. 2023

Chin Med

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“…This can assist practitioners in identifying potential correlations between syndromes and acupoints, leading to more accurate diagnoses and effective treatment outcomes. Deep learning is another powerful tool within the broader field of AI that can discover the combination rules and pharmacodynamic characteristics of TCM [44] . It helps doctors stratify patient risks [45] , examine histopathological images [46] , and improve their understanding of diseases [47] .…”

Section: Data-driven Acupuncture Prescription: From Evidence To Practicementioning

confidence: 99%

Intelligent Acupuncture: Data-driven Revolution of Traditional Chinese Medicine

Bao,

Ding,

Zhang

et al. 2023

Acupuncture and Herbal Medicine

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Acupuncture, a form of traditional Chinese medicine with a history of 2000 years in China, has gained wider acceptance worldwide as a complementary therapy. Studies have examined its effectiveness in various health conditions and it is commonly used alongside conventional medical treatments. With the development of artificial intelligence (AI) technology, new possibilities for improving the efficacy and precision of acupuncture have emerged. This study explored the combination of traditional acupuncture and AI technology from three perspectives: acupuncture diagnosis, prescription, and treatment evaluation. The study aimed to provide cutting-edge direction and theoretical assistance for the development of an acupuncture robot.

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“…Additionally, compared to single-target diagnosis, multitarget diagnosis enhances sensitivity and specificity. It focuses on multiple disease-related markers, enabling the detection of a wider range of disease-related alterations and minimizing the risk of false negatives or false positives. , However, successful implementation of multitarget diagnosis necessitates careful marker selection, validation, and integration using appropriate analytical methods and algorithms, in conjunction with machine learning techniques. − Machine learning algorithms examine large-scale data sets, detecting subtle patterns and abnormalities that help to make precise diagnoses, while reducing human error …”

mentioning

confidence: 99%

Accurate and Convenient Lung Cancer Diagnosis through Detection of Extracellular Vesicle Membrane Proteins via Förster Resonance Energy Transfer

Xiao,

Yao,

Liao

et al. 2023

Nano Lett.

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Tumor-derived extracellular vesicles (EVs) are promising to monitor early stage cancer. Unfortunately, isolating and analyzing EVs from a patient's liquid biopsy are challenging. For this, we devised an EV membrane proteins detection system (EV-MPDS) based on Forster resonance energy transfer (FRET) signals between aptamer quantum dots and AIEgen dye, which eliminated the EV extraction and purification to conveniently diagnose lung cancer. In a cohort of 80 clinical samples, this system showed enhanced accuracy (100% versus 65%) and sensitivity (100% versus 55%) in cancer diagnosis as compared to the ELISA detection method. Improved accuracy of early screening (from 96.4% to 100%) was achieved by comprehensively profiling five biomarkers using a machine learning analysis system. FRET-based tumor EV-MPDS is thus an isolation-free, low-volume (1 μL), and highly accurate approach, providing the potential to aid lung cancer diagnosis and early screening.

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Deep learning and machine intelligence: New computational modeling techniques for discovery of the combination rules and pharmacodynamic characteristics of Traditional Chinese Medicine

Cited by 21 publications

References 87 publications

Machine learning in TCM with natural products and molecules: current status and future perspectives

Machine learning in TCM with natural products and molecules: current status and future perspectives

Intelligent Acupuncture: Data-driven Revolution of Traditional Chinese Medicine

Accurate and Convenient Lung Cancer Diagnosis through Detection of Extracellular Vesicle Membrane Proteins via Förster Resonance Energy Transfer

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