Identification of gene expression signatures across different types of neural stem cells with the Monte‐Carlo feature selection method

2017 ◽  
Vol 119 (4) ◽  
pp. 3394-3403 ◽  
Author(s):  
Lei Chen ◽  
JiaRui Li ◽  
Yu‐Hang Zhang ◽  
KaiYan Feng ◽  
ShaoPeng Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Monte Carlo ◽  
Feature Selection ◽  
Neural Stem Cells ◽  
Feature Selection Method ◽  
Selection Method ◽  
Different Types ◽  
Gene Expression Signatures ◽  
Monte Carlo Feature Selection

scholarly journals A Robust Gene selection Method for Microarray-based Cancer Classification

2010 ◽  
Vol 9 ◽  
pp. CIN.S3794 ◽  
Author(s):  
Xiaosheng Wang ◽  
Osamu Gotoh
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Information Gain ◽  
Expression Profiles ◽  
Feature Selection Method ◽  
Gene Expression Profiles ◽  
Molecular Classification ◽  
Selection Method ◽  
Chi Square

Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. We investigated the properties of one feature selection approach proposed in our previous work, which was the generalization of the feature selection method based on the depended degree of attribute in rough sets. We compared the feature selection method with the established methods: the depended degree, chi-square, information gain, Relief-F and symmetric uncertainty, and analyzed its properties through a series of classification experiments. The results revealed that our method was superior to the canonical depended degree of attribute based method in robustness and applicability. Moreover, the method was comparable to the other four commonly used methods. More importantly, the method can exhibit the inherent classification difficulty with respect to different gene expression datasets, indicating the inherent biology of specific cancers.

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