scholarly journals A Novel Method for Evaluating the Cardiotoxicity of Traditional Chinese Medicine Compatibility by Using Support Vector Machine Model Combined with Metabonomics

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Yubo Li ◽  
Haonan Zhou ◽  
Jiabin Xie ◽  
Mayassa Salum Ally ◽  
Zhiguo Hou ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Sensitivity And Specificity ◽  
High Sensitivity ◽  
Support Vector ◽  
Machine Model ◽  
Svm Model ◽  
Long Time ◽  
Novel Method

Traditional biochemical and histopathological tests have been used to evaluate the safety of traditional Chinese medicine (TCM) compatibility for a long time. But these methods lack high sensitivity and specificity. In the previous study, we have found ten biomarkers related to cardiotoxicity and established a support vector machine (SVM) prediction model. Results showed a good sensitivity and specificity. Therefore, in this study, we used SVM model combined with metabonomics UPLC/Q-TOF-MS technology to build a rapid and sensitivity and specificity method to predict the cardiotoxicity of TCM compatibility. This study firstly applied SVM model to the prediction of cardiotoxicity in TCM compatibility containingAconiti Lateralis Radix Praeparataand further identified whether the cardiotoxicity increased afterAconiti Lateralis Radix Praeparatacombined with other TCM. This study provides a new idea for studying the evaluation of the cardiotoxicity caused by compatibility of TCM.

scholarly journals Drug Design of Cyclin-Dependent Kinase 2 Inhibitor for Melanoma from Traditional Chinese Medicine

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Hsin-Chieh Tang ◽  
Calvin Yu-Chian Chen
Keyword(s):  
Cell Cycle ◽  
Molecular Dynamics ◽  
Support Vector Machine ◽  
Systems Biology ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Md Simulation ◽  
Support Vector ◽  
Cyclin Dependent Kinase ◽  
Melanoma Therapy

One has found an important cell cycle controller. This guard can decide the cell cycle toward proliferation or quiescence. Cyclin-dependent kinase 2 (CDK2) is a unique target among the CDK family in melanoma therapy. We attempted to find out TCM compounds from TCM Database@Taiwan that have the ability to inhibit the activity of CDK2 by systems biology. We selected Tetrahydropalmatine, Reserpiline, and (+)-Corydaline as the candidates by docking and screening results for further survey. We utilized support vector machine (SVM), multiple linear regression (MLR) models and Bayesian network for validation of predicted activity. By overall analysis of docking results, predicted activity, and molecular dynamics (MD) simulation, we could conclude that Tetrahydropalmatine, Reserpiline, and (+)-Corydaline had better binding affinity than the control. All of them had the ability to inhibit the activity of CDK2 and might have the opportunity to be applied in melanoma therapy.

scholarly journals An Enhanced Support Vector Machine Model for Classification of Transit-Oriented Development

2021 ◽  
Author(s):  
Rupali Khare ◽  
Vasanta Govind kumar Villuri ◽  
Devarshi Chaurasia
Keyword(s):  
Support Vector Machine ◽  
Urban Planning ◽  
Regularization Parameter ◽  
Spatial Association ◽  
Support Vector ◽  
Transit Oriented Development ◽  
Machine Model ◽  
Urban Planner ◽  
Svm Model ◽  
Tenfold Cross Validation

Abstract Transit-oriented development (TOD) can invigorate sustainable development by conveying a more coordinated transit and surrounding land use. Given the dynamic nature of urban community development, urban planners find it hard to precisely respond to questions such as where TOD planning around the transit hubs can succeed in the city. The present study proposes a framework utilizing a support vector machine (SVM) to enhance the TODness prediction of an area to address this issue. An SVM model has successfully applied to 16 bus rapid transit station areas in Bhopal city, India, using the tenfold cross-validation resampling methods and thirteen predictor variables. The models performance was in good agreement with 93.75% precision, utilizing the sigmoid kernel function and the regularization parameter esteem equivalent to 4. This methodology could be used at any scale, and the outcomes could offer recommendations for more accurate urban planning, fortifying the relationship between TOD and spatial association. The study provides the basis for predicting better future TODness classification, which will help the urban planner for sustainable urban planning and policy making.

scholarly journals A Novel Method for Recognizing Vietnamese Voice Commands on Smartphones with Support Vector Machine and Convolutional Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Quang H. Nguyen ◽  
Tuan-Dung Cao
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Approach ◽  
Voice Command ◽  
Svm Model ◽  
Machine Learning Approach ◽  
Novel Method ◽  
Correction System

This paper will present a new method of identifying Vietnamese voice commands using Google speech recognition (GSR) service results. The problem is that the percentage of correct identifications of Vietnamese voice commands in the Google system is not high. We propose a supervised machine-learning approach to address cases in which Google incorrectly identifies voice commands. First, we build a voice command dataset that includes hypotheses of GSR for each corresponding voice command. Next, we propose a correction system using support vector machine (SVM) and convolutional neural network (CNN) models. The results show that the correction system reduces errors in recognizing Vietnamese voice commands from 35.06% to 7.08% using the SVM model and 5.15% using the CNN model.

scholarly journals A Support Vector Machine Model with Hyperparameters Optimised by Mind Evolutionary Algorithm for Assessing Permeability of Rock

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Wenjin Zhu ◽  
Zhiming Chao ◽  
Guotao Ma
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Evolutionary Algorithm ◽  
Predictive Accuracy ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Learning Models ◽  
Machine Model ◽  
Svm Model ◽  
Machine Learning Models

In this paper, a database developed from the existing literature about permeability of rock was established. Based on the constructed database, a Support Vector Machine (SVM) model with hyperparameters optimised by Mind Evolutionary Algorithm (MEA) was proposed to predict the permeability of rock. Meanwhile, the Genetic Algorithm- (GA-) and Particle Swarm Algorithm- (PSO-) SVM models were constructed to compare the improving effects of MEA on the foretelling accuracy of machine learning models with those of GA and PSO, respectively. The following conclusions were drawn. MEA can increase the predictive accuracy of the constructed machine learning models remarkably in a few iteration times, which has better optimisation performance than that of GA and PSO. MEA-SVM has the best forecasting performance, followed by PSO-SVM, while the estimating precision of GA-SVM is lower than them. The proposed MEA-SVM model can accurately predict the permeability of rock indicating the model having a satisfactory generalization and extrapolation capacity.

scholarly journals A Network Pharmacology-Based Approach to Investigate the Novel TCM Formula against Huntington’s Disease and Validated by Support Vector Machine Model

2018 ◽  
Vol 2018 ◽  
pp. 1-29 ◽  
Author(s):  
Wenjie Dai ◽  
Hsin-Yi Chen ◽  
Calvin Yu-Chian Chen
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Huntington's Disease ◽  
Huntington’S Disease ◽  
Rational Design ◽  
Binding Capacity ◽  
Quantitative Structure Activity Relationship ◽  
Network Pharmacology ◽  
Support Vector ◽  
Machine Model

Several pathways are crucial in Huntington’s disease (HD). Based on the concept of multitargets, network pharmacology-based analysis was employed to find out related proteins in disease network. The network target method aims to find out related mechanism of efficacy substances in rational design way. Traditional Chinese medicine prescriptions would be used for research and development against HD. Virtual screening was performed to obtain drug molecules with high binding capacity from traditional Chinese medicine (TCM) database@Taiwan. Quantitative structure-activity relationship (QSAR) models were conducted by MLR, SVM, CoMFA, and CoMSIA, constructed to predict the bioactivities of candidates. The compounds with high-dock score were further analyzed compared with control. Traditional Chinese medicine reported in the literature could be the training set provided for constructing novel formula by SVM model. We tried to find a novel formula that can bind well with these targets at the same time, which indicates our design could be highly related to the HD. Additionally, the candidates would validate by a long-term molecular dynamics (MD) simulation, 5 microseconds. Thus, we suggested the herbs Brucea javanica, Holarrhena antidysenterica, Dichroa febrifuga, Erythrophleum guineense, etc. which contained active compounds might be a novel medicine formula toward Huntington’s disease.

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