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RSS FeedsIJMS, Vol. 23, Pages 9087: Feature Selection and Molecular Classification of Cancer Phenotypes: A Comparative Study (International Journal of Molecular Sciences)

 
 

13 august 2022 13:39:55

 
IJMS, Vol. 23, Pages 9087: Feature Selection and Molecular Classification of Cancer Phenotypes: A Comparative Study (International Journal of Molecular Sciences)
 


The classification of high dimensional gene expression data is key to the development of effective diagnostic and prognostic tools. Feature selection involves finding the best subset with the highest power in predicting class labels. Here, we conducted a comparative study focused on different combinations of feature selectors (Chi-Squared, mRMR, Relief-F, and Genetic Algorithms) and classification learning algorithms (Random Forests, PLS-DA, SVM, Regularized Logistic/Multinomial Regression, and kNN) to identify those with the best predictive capacity. The performance of each combination is evaluated through an empirical study on three benchmark cancer-related microarray datasets. Our results first suggest that the quality of the data relevant to the target classes is key for the successful classification of cancer phenotypes. We also proved that, for a given classification learning algorithm and dataset, all filters have a similar performance. Interestingly, filters achieve comparable or even better results with respect to the GA-based wrappers, while also being easier and faster to implement. Taken together, our findings suggest that simple, well-established feature selectors in combination with optimized classifiers guarantee good performances, with no need for complicated and computationally demanding methodologies.


 
118 viewsCategory: Biochemistry, Biophysics, Molecular Biology
 
IJMS, Vol. 23, Pages 9088: Physiological and Pathological Functions of Neuronal Hemoglobin: A Key Underappreciated Protein in Parkinson’s Disease (International Journal of Molecular Sciences)
IJMS, Vol. 23, Pages 9092: Musculoskeletal Development and Skeletal Pathophysiology’s (International Journal of Molecular Sciences)
 
 
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