원문정보
초록
영어
Microarray technology provides a way for researchers to measure the expression level of thousands of genes simultaneously in a single experiment. Due to the increasing amount of microarray data, the field of microarray data analysis has become a major topic among researchers. One of the examples of microarray data analysis is classification. Classification is the process of determining the classes for samples. The goal of classification is to identify the differentially expressed genes so that these genes can be used to predict the classes for new samples. In order to perform the tasks of classification of microarray data, classification software is required for effective classification and analysis of large-scale data. This paper reviews numerous classification software applications for gene expression data. In this paper, the reviewed software can be categorized into six supervised classification methods: Support Vector Machine, K-Nearest Neighbour, Neural Network, Linear Discriminant Analysis, Bayesian Classifier, and Random Forest.
목차
1. Introduction
2. Software for Support Vector Machine (SVM)
2.1 LIBSVM
2.2 SVMlight
2.3. SVMTorch
2.4 mySVM
2.5 Weka-LibSVM (WLSVM)
2.6 BSVM
2.7 TinySVM
2.8 SVM in R (e1071)
2.9 LSVM
2.10 PyML
2.11 PSVM
2.12 MSVMpack
2.13 Summary of SVM Software
3. Software for K-Nearest Neighbour (KNN)
3.1 Mayday Software
3.2 kknn
3.3 knnGarden
3.4 Weka-KNN
3.5 rknn
3.6 ArrayMinerClassMaker
3.7 BRB-Array Tools
3.8 Summary ofKNN Software
4. Software for Neural Networks (NN)
4.1 Pattern Classification Program (PCP)
4.2 nnet
4.3 neuralnet
4.4 pnn
4.5 RSSNS
4.6 Summary of Neural Networks Software
5. Bayesian Classifier Software
5.1 Iterative Bayesian Model Averaging
5.2 Full Bayesian Network Classifier
5.3 Bayesian Trans-Dimensional Sampling
5.4 Bayesian Stochastic Search Variable Selection
5.5 Naïve Bayes Classifier
5.6 Summary of Bayesian Classifier Software
6. Software for Linear Discriminant Analysis (LDA)
6.1 Regularized LDA
6.2 Sparse Discriminant Analysis
6.3 Robust Regularized LDA
6.4. Summary ofLDA Software
7. Random Forest (RF)
7.1 Backward Elimination Random Forest
7.2 Online Random Forest
7.3 cforest
7.4 Guided Regularized Random Forest
7.5 Big Random Forest
7.6 Summary of Random Forest Software
8. Conclusion
Acknowledgements
References