The use of support vector machine and genetic algorithms to predict protein function

2012 ◽  
Author(s):  
Walkiria K. Resende ◽  
Renato A. Nascimento ◽  
Carolina R. Xavier ◽  
Iara F. Lopes ◽  
Cristiane N. Nobre
Keyword(s):  
Support Vector Machine ◽  
Genetic Algorithms ◽  
Protein Function ◽  
Support Vector ◽  
Predict Protein Function

PROTEIN FOLD CLASSIFICATION WITH GENETIC ALGORITHMS AND FEATURE SELECTION

2009 ◽  
Vol 07 (05) ◽  
pp. 773-788 ◽  
Author(s):  
PENG CHEN ◽  
CHUNMEI LIU ◽  
LEGAND BURGE ◽  
MOHAMMAD MAHMOOD ◽  
WILLIAM SOUTHERLAND ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Genetic Algorithms ◽  
Feature Selection ◽  
Training Dataset ◽  
Support Vector ◽  
Protein Fold ◽  
Classification Rate ◽  
Test Dataset ◽  
Feature Vectors ◽  
Protein Folds

Protein fold classification is a key step to predicting protein tertiary structures. This paper proposes a novel approach based on genetic algorithms and feature selection to classifying protein folds. Our dataset is divided into a training dataset and a test dataset. Each individual for the genetic algorithms represents a selection function of the feature vectors of the training dataset. A support vector machine is applied to each individual to evaluate the fitness value (fold classification rate) of each individual. The aim of the genetic algorithms is to search for the best individual that produces the highest fold classification rate. The best individual is then applied to the feature vectors of the test dataset and a support vector machine is built to classify protein folds based on selected features. Our experimental results on Ding and Dubchak's benchmark dataset of 27-class folds show that our approach achieves an accuracy of 71.28%, which outperforms current state-of-the-art protein fold predictors.

Parameter Optimization for Support Vector Machine Based on Nested Genetic Algorithms

2015 ◽  
Vol 3 (6) ◽  
pp. 507-511 ◽  
Author(s):  
Pin Liao ◽  
Xin Zhang ◽  
Kunlun Li ◽  
Yang Fu ◽  
Mingyan Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Genetic Algorithms ◽  
Parameter Optimization ◽  
Support Vector

Modeling and Simulation of the GA_LSSVM-Based Soft Measurement for Torque

2010 ◽  
Vol 44-47 ◽  
pp. 733-737
Author(s):  
Zhen Chen ◽  
An Yi Huang
Keyword(s):  
Support Vector Machine ◽  
Genetic Algorithms ◽  
Modeling And Simulation ◽  
Traditional Method ◽  
Measurement Accuracy ◽  
Cross Validation ◽  
Measurement Methods ◽  
Small Sample ◽  
Support Vector ◽  
Soft Measurement

Given the traditional method of direct measurement which is of high cost, difficult installation and poor reliability,this paper is presented a new model of the torque soft measure method based on least squares support vector machine using genetic algorithms optimization:genetic algorithms replaces the previous cross-validation method for model parameter’s optimization, in order to avoid the blindness of the parameter choices.Verified by simulation, the model can effectively address the deficiencies of traditional measurement methods and obtain better measurement accuracy and speed , possessing benefits of an outstanding ability for small sample study and being easy to compute.

Prediction of Classification of Rock Burst Risk Based on Genetic Algorithms with SVM

2014 ◽  
Vol 628 ◽  
pp. 383-389 ◽  
Author(s):  
Ya Hui Peng ◽  
Kang Peng ◽  
Jian Zhou ◽  
Zhi Xiang Liu
Keyword(s):  
Support Vector Machine ◽  
Genetic Algorithms ◽  
Rock Burst ◽  
Support Vector ◽  
Proposed Model ◽  
Svm Model ◽  
Rock Burst Risk ◽  
Rock Burst Prediction ◽  
Optimization Search

Due to the complex features of rock burst hazard assessment systems, a support vector machine (SVM) model for predicting of classification of rock burst was established based on the SVM theory and the actual characteristics of the project in this study. The main factors of rock burst, such as coal seam, dip, buried depth, structure situation, change of pitch angle, change of coal thickness, gas concentration, roof management, pressure relief and shooting were defined as the criterion indices for rock burst prediction in the proposed model. In order to determine reasonable and efficient the parameters of SVM, Firstly, the appropriate fitness function for genetic algorithms (GA) operation was determined, and then optimization parameters of SVM model were selected by real coded GA, therefore, the genetic algorithms and support vector machine (GSVM) model was established. A GSVM model was obtained through training 23 sets of measured data, the cross-validation method was introduced to verify the stability of GSVM model and the ratio of mis-discrimination is 0. Moreover, the proposed model was used to predict 12 new samples rock burst, the correct rate of prediction results is 91.6667% and are identical with actual situation. The results show that the genetic algorithm can speed up SVM parameter optimization search, the proposed model has a high credibility in the study of rock burst prediction of risk classification, which can be applied to practical engineering.

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