Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees.
Hsiu-Ling ChouChung-Tay YaoSui-Lun SuChia-Yi LeeKuang-Yu HuHarn-Jing TerngYun-Wen ShihYu-Tien ChangYu-Fen LuChi-Wen ChangMark L. WahlqvistThomas WetterChi-Ming ChuPublished in: BMC Bioinform. (2013)
Keyphrases
- logistic regression
- breast cancer
- decision trees
- microarray
- artificial neural networks
- cancer classification
- microarray data
- gene expression
- naive bayes
- random forest
- high throughput
- gene expression data
- gene selection
- neural network
- chi square
- cancer diagnosis
- experimental conditions
- high dimensionality
- training data
- clustering analysis
- dna microarray
- gene ontology
- gene expression profiles
- microarray datasets
- machine learning
- multi layer perceptron
- base classifiers
- classification models
- training set
- fold cross validation
- feature selection
- bayesian classifier
- biological data
- semi supervised