scholarly journals A hybrid gene selection algorithm for microarray cancer classification using genetic algorithm and learning automata

2017 ◽  
Vol 9 ◽  
pp. 246-254 ◽  
Author(s):  
Habib Motieghader ◽  
Ali Najafi ◽  
Balal Sadeghi ◽  
Ali Masoudi-Nejad
Keyword(s):  
Genetic Algorithm ◽  
Gene Selection ◽  
Learning Automata ◽  
Cancer Classification ◽  
Selection Algorithm ◽  
Hybrid Gene ◽  
Gene Selection Algorithm

scholarly journals Brain Cancer Prediction Based on Novel Interpretable Ensemble Gene Selection Algorithm and Classifier

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1936
Author(s):  
Abdulqader M. Almars ◽  
Majed Alwateer ◽  
Mohammed Qaraad ◽  
Souad Amjad ◽  
Hanaa Fathi ◽  
...  
Keyword(s):  
Gene Selection ◽  
Ensemble Methods ◽  
Cancer Classification ◽  
Biological Information ◽  
Gradient Boosting ◽  
New Approach ◽  
Biological Studies ◽  
Abnormal Cells ◽  
Gene Selection Algorithm ◽  
Gene Expression Levels

The growth of abnormal cells in the brain causes human brain tumors. Identifying the type of tumor is crucial for the prognosis and treatment of the patient. Data from cancer microarrays typically include fewer samples with many gene expression levels as features, reflecting the curse of dimensionality and making classifying data from microarrays challenging. In most of the examined studies, cancer classification (Malignant and benign) accuracy was examined without disclosing biological information related to the classification process. A new approach was proposed to bridge the gap between cancer classification and the interpretation of the biological studies of the genes implicated in cancer. This study aims to develop a new hybrid model for cancer classification (by using feature selection mRMRe as a key step to improve the performance of classification methods and a distributed hyperparameter optimization for gradient boosting ensemble methods). To evaluate the proposed method, NB, RF, and SVM classifiers have been chosen. In terms of the AUC, sensitivity, and specificity, the optimized CatBoost classifier performed better than the optimized XGBoost in cross-validation 5, 6, 8, and 10. With an accuracy of 0.91±0.12, the optimized CatBoost classifier is more accurate than the CatBoost classifier without optimization, which is 0.81± 0.24. By using hybrid algorithms, SVM, RF, and NB automatically become more accurate. Furthermore, in terms of accuracy, SVM and RF (0.97±0.08) achieve equivalent and higher classification accuracy than NB (0.91±0.12). The findings of relevant biomedical studies confirm the findings of the selected genes.

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