Neighborhood Rough Set Model Based Gene Selection for Multi-subtype Tumor Classification

Author(s):  
Shulin Wang ◽  
Xueling Li ◽  
Shanwen Zhang
2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Mei-Ling Hou ◽  
Shu-Lin Wang ◽  
Xue-Ling Li ◽  
Ying-Ke Lei

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.


Genomics ◽  
2018 ◽  
Vol 110 (1) ◽  
pp. 10-17 ◽  
Author(s):  
M. Dashtban ◽  
Mohammadali Balafar ◽  
Prashanth Suravajhala

2017 ◽  
Vol 67 ◽  
pp. 59-68 ◽  
Author(s):  
Yumin Chen ◽  
Zunjun Zhang ◽  
Jianzhong Zheng ◽  
Ying Ma ◽  
Yu Xue

2019 ◽  
Vol 5 (3) ◽  
pp. 329-347 ◽  
Author(s):  
Rachid Benouini ◽  
Imad Batioua ◽  
Soufiane Ezghari ◽  
Khalid Zenkouar ◽  
Azeddine Zahi

2021 ◽  
pp. 107223
Author(s):  
Binbin Sang ◽  
Hongmei Chen ◽  
Lei Yang ◽  
Tianrui Li ◽  
Weihua Xu ◽  
...  

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