Support vector machine models in drug design: applications to drug transport processes and QSAR using simplex optimisations and variable selection

2003 ◽  
Vol 55 (1-2) ◽  
pp. 337-346 ◽  
Author(s):  
Ulf Norinder
Keyword(s):  
Support Vector Machine ◽  
Drug Design ◽  
Variable Selection ◽  
Drug Transport ◽  
Transport Processes ◽  
Support Vector

Hazard identification and prediction system for aircraft electrical system based on SRA and SVM

2020 ◽  
Vol 234 (4) ◽  
pp. 1014-1026 ◽  
Author(s):  
Di Zhou ◽  
Xiao Zhuang ◽  
Hongfu Zuo ◽  
Jing Cai ◽  
Han Bao
Keyword(s):  
Support Vector Machine ◽  
Variable Selection ◽  
Relative Error ◽  
Hazard Identification ◽  
Identification Accuracy ◽  
Normal Operation ◽  
Support Vector ◽  
Prediction System ◽  
Electrical System ◽  
Bus Voltage

The aircraft electrical system provides power for the normal operation of the aircraft. Its normal operation is critical to ensure the safe flight of the aircraft. Therefore, it is very important to identify the hazards in the aircraft electrical system. In this paper, a hazard identification and prediction system which can intelligently identify potential hazards in aircraft electrical system is proposed. The proposed hazard identification and prediction system mainly includes three processes: variable selection, hazard identification, and hazard prediction. In the process of variable selection, the stepwise regression analysis is used to select 8 main parameters that have the major influence on the DC bus voltage value from 18 parameters. In the process of hazard identification, support vector machine is used to identify pre-existing hazards in electrical system based on the status of all components. The identification accuracy of the support vector machine is 92.3%. When the electrical system does not have unacceptable hazards, a prediction of the variation range of the DC bus voltage value in the aircraft electrical system is performed. The average prediction relative error of support vector machine is only 0.86%. Overall, the identification accuracy and average prediction relative error show that the proposed hazard identification and prediction system can accurately and effectively identify and predict the hazards in the aircraft electrical system.

Variable Selection in Proteomic Profile Classification by Interval Support Vector Machines (iSVM)

2014 ◽  
Vol 556-562 ◽  
pp. 347-350
Author(s):  
Xiao Li Yang ◽  
Huan Yun He
Keyword(s):  
Support Vector Machine ◽  
Variable Selection ◽  
Support Vector ◽  
Proteomic Profile ◽  
Error Level ◽  
Full Spectrum ◽  
Local Modeling ◽  
Svm Model ◽  
Vector Machines ◽  
Selection For

For variable selection in proteomic profile classification, we present a new local modeling procedure called interval support vector machine (iSVM). This procedure builds a series of SVM models in a window that moves over the whole spectral region and then locates useful spectral intervals in terms of the least complexity of SVM models reaching a desired error level. We applied iSVM in variable selection for proteomic profile classification. The results show that the proposed procedure are very promising for classification target-based variable selection and obtain much better classification than full-spectrum SVM model.

Variable selection for support vector machine based multisensor systems

2007 ◽  
Vol 122 (1) ◽  
pp. 259-268 ◽  
Author(s):  
O. Gualdrón ◽  
J. Brezmes ◽  
E. Llobet ◽  
A. Amari ◽  
X. Vilanova ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Variable Selection ◽  
Support Vector ◽  
Multisensor Systems ◽  
Selection For

scholarly journals Predicting P-Glycoprotein-Mediated Drug Transport Based On Support Vector Machine and Three-Dimensional Crystal Structure of P-glycoprotein

PLoS ONE ◽  
2011 ◽  
Vol 6 (10) ◽  
pp. e25815 ◽  
Author(s):  
Zsolt Bikadi ◽  
Istvan Hazai ◽  
David Malik ◽  
Katalin Jemnitz ◽  
Zsuzsa Veres ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Support Vector Machine ◽  
Drug Transport ◽  
Three Dimensional ◽  
Support Vector ◽  
P Glycoprotein ◽  
Dimensional Crystal

Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms

2018 ◽  
Vol 21 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Rajnish Kumar ◽  
Anju Sharma ◽  
Mohammed Haris Siddiqui ◽  
Rajesh Kumar Tiwari
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Drug Design ◽  
Protein Binding ◽  
Plasma Protein Binding ◽  
Plasma Protein ◽  
Drug Distribution ◽  
Support Vector ◽  
Design And Development ◽  
Drug Candidates

Aim and Objective: Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug distribution properties should be considered at the initial phases of the drug design and development. Therefore, PPB prediction models are receiving an increased attention. Materials and Methods: In the current study, we present a systematic approach using Support vector machine, Artificial neural network, k- nearest neighbor, Probabilistic neural network, Partial least square and Linear discriminant analysis to relate various in vitro and in silico molecular descriptors to a diverse dataset of 736 drugs/drug-like compounds. Results: The overall accuracy of Support vector machine with Radial basis function kernel came out to be comparatively better than the rest of the applied algorithms. The training set accuracy, validation set accuracy, precision, sensitivity, specificity and F1 score for the Suprort vector machine was found to be 89.73%, 89.97%, 92.56%, 87.26%, 91.97% and 0.898, respectively. Conclusion: This model can potentially be useful in screening of relevant drug candidates at the preliminary stages of drug design and development.

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