Earticle

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

Abstract
 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

키워드

저자

• Tan Ching Siang [ Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, UniversitiTeknologi Malaysia, 81310 Skudai, Johor ]
• Ting Wai Soon [ Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, UniversitiTeknologi Malaysia, 81310 Skudai, Johor ]
• Shahreen Kasim [ Faculty of Computer Science and Information Technology, UniversitiTun Hussein Onn Malaysia, 86400 Parit Raja, BatuPahat, Johor ]
• Mohd Saberi Mohamad [ Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, UniversitiTeknologi Malaysia, 81310 Skudai, Johor. ]
• Chan Weng Howe [ Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, UniversitiTeknologi Malaysia, 81310 Skudai, Johor. ]
• Safaai Deris [ Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, UniversitiTeknologi Malaysia, 81310 Skudai, Johor. ]
• Zalmiyah Zakaria [ Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, UniversitiTeknologi Malaysia, 81310 Skudai, Johor. ]
• Zuraini Ali Shah [ Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, UniversitiTeknologi Malaysia, 81310 Skudai, Johor. ]
• Zuwairie Ibrahim [ Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang. ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605