scholarly journals Hybrid Correlation based Gene Selection for Accurate Cancer Classification of Gene Expression Data

2012 ◽  
Vol 43 (14) ◽  
pp. 13-18 ◽  
Author(s):  
Vibhav PrakashSingh ◽  
Singh Gaurav Arvind ◽  
Arindam G Mahapatra
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Cancer Classification ◽  
Expression Data ◽  
Selection For

scholarly journals Fine-grained parallelization of fitness functions in bioinformatics optimization problems: gene selection for cancer classification and biclustering of gene expression data

2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Juan A. Gomez-Pulido ◽  
Jose L. Cerrada-Barrios ◽  
Sebastian Trinidad-Amado ◽  
Jose M. Lanza-Gutierrez ◽  
Ramon A. Fernandez-Diaz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Optimization Problems ◽  
Cancer Classification ◽  
Expression Data ◽  
Fine Grained ◽  
Fitness Functions ◽  
Selection For

A model for gene selection and classification of gene expression data

2007 ◽  
Vol 11 (2) ◽  
pp. 219-222 ◽  
Author(s):  
Mohd Saberi Mohamad ◽  
Sigeru Omatu ◽  
Safaai Deris ◽  
Siti Zaiton Mohd Hashim
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Expression Data

scholarly journals A Robust Procedure for Machine Learning Algorithms Using Gene Expression Data

2021 ◽  
Vol 12 (2) ◽  
pp. 2422-2439
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Gene Expression Data ◽  
Learning Algorithms ◽  
Simulated Data ◽  
Cancer Classification ◽  
Machine Learning Algorithms ◽  
Expression Data ◽  
Traditional Procedure

Cancer classification is one of the main objectives for analyzing big biological datasets. Machine learning algorithms (MLAs) have been extensively used to accomplish this task. Several popular MLAs are available in the literature to classify new samples into normal or cancer populations. Nevertheless, most of them often yield lower accuracies in the presence of outliers, which leads to incorrect classification of samples. Hence, in this study, we present a robust approach for the efficient and precise classification of samples using noisy GEDs. We examine the performance of the proposed procedure in a comparison of the five popular traditional MLAs (SVM, LDA, KNN, Naïve Bayes, Random forest) using both simulated and real gene expression data analysis. We also considered several rates of outliers (10%, 20%, and 50%). The results obtained from simulated data confirm that the traditional MLAs produce better results through our proposed procedure in the presence of outliers using the proposed modified datasets. The further transcriptome analysis found the significant involvement of these extra features in cancer diseases. The results indicated the performance improvement of the traditional MLAs with our proposed procedure. Hence, we propose to apply the proposed procedure instead of the traditional procedure for cancer classification.

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