A Method for Cancer Classification Using Ensemble Neural Networks with Gene Expression Profile

2008 ◽  
Author(s):  
Xiaogang Ruan ◽  
Jinlian Wang ◽  
Hui Li ◽  
Xiaoming Li
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Cancer Classification ◽  
Ensemble Neural Networks

scholarly journals Erratum to: “47glioblastoma Gene Expression Profile Diagnostics by the Artificial Neural Networks”

2010 ◽  
Vol 19 (4) ◽  
pp. 344-344
Author(s):  
Y. A. Kuperin ◽  
A. A. Mekler ◽  
I. Kniazeva ◽  
D. R. Schwartz ◽  
V. V. Dmitrenko ◽  
...  
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Artificial Neural

scholarly journals 47Glioblastoma gene expression profile diagnostics by the artificial neural networks

2010 ◽  
Vol 19 (2) ◽  
pp. 181-186 ◽  
Author(s):  
A. A. Mekler ◽  
I. Knyazeva ◽  
D. R. Schwartz ◽  
Y. A. Kuperin ◽  
V. V. Dmitrenko ◽  
...  
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Artificial Neural

scholarly journals Neighborhood Rough Set Reduction-Based Gene Selection and Prioritization for Gene Expression Profile Analysis and Molecular Cancer Classification

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Mei-Ling Hou ◽  
Shu-Lin Wang ◽  
Xue-Ling Li ◽  
Ying-Ke Lei
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Rough Set ◽  
Gene Selection ◽  
Cancer Biomarkers ◽  
Cancer Classification ◽  
Tumor Classification ◽  
Cancer Type ◽  
Neighborhood Rough Set

Selection of reliable cancer biomarkers is crucial for gene expression profile-based precise diagnosis of cancer type and successful treatment. However, current studies are confronted with overfitting and dimensionality curse in tumor classification and false positives in the identification of cancer biomarkers. Here, we developed a novel gene-ranking method based on neighborhood rough set reduction for molecular cancer classification based on gene expression profile. Comparison with other methods such as PAM, ClaNC, Kruskal-Wallis rank sum test, and Relief-F, our method shows that only few top-ranked genes could achieve higher tumor classification accuracy. Moreover, although the selected genes are not typical of known oncogenes, they are found to play a crucial role in the occurrence of tumor through searching the scientific literature and analyzing protein interaction partners, which may be used as candidate cancer biomarkers.

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