MRMR-GWICA: A hybrid gene selection and ensemble clustering framework for breast cancer gene expression data

Karthik, D.; Kalaiselvi, K.

doi:10.1063/5.0074273

Cited by 1 publication

(1 citation statement)

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“…Several research papers have been published, in which various artificial intelligence (AI) methods were applied to the breast cancer CUMIDA dataset [ 1 , 2 ] for breast cancer classification. In [ 3 ], the authors used a method for gene selection based on minimal redundancy maximal significance that is integrated into the gene weight imperialist competitive algorithm. Using this approach, the insightful genes from the micro-array-containing profile were classified.…”

Section: Introductionmentioning

confidence: 99%

Development of Symbolic Expressions Ensemble for Breast Cancer Type Classification Using Genetic Programming Symbolic Classifier and Decision Tree Classifier

Anđelić

Šegota

2023

Cancers

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Breast cancer is a type of cancer with several sub-types. It occurs when cells in breast tissue grow out of control. The accurate sub-type classification of a patient diagnosed with breast cancer is mandatory for the application of proper treatment. Breast cancer classification based on gene expression is challenging even for artificial intelligence (AI) due to the large number of gene expressions. The idea in this paper is to utilize the genetic programming symbolic classifier (GPSC) on the publicly available dataset to obtain a set of symbolic expressions (SEs) that can classify the breast cancer sub-type using gene expressions with high classification accuracy. The initial problem with the used dataset is a large number of input variables (54,676 gene expressions), a small number of dataset samples (151 samples), and six classes of breast cancer sub-types that are highly imbalanced. The large number of input variables is solved with principal component analysis (PCA), while the small number of samples and the large imbalance between class samples are solved with the application of different oversampling methods generating different dataset variations. On each oversampled dataset, the GPSC with random hyperparameter values search (RHVS) method is trained using 5-fold cross validation (5CV) to obtain a set of SEs. The best set of SEs is chosen based on mean values of accuracy (ACC), the area under the receiving operating characteristic curve (AUC), precision, recall, and F1-score values. In this case, the highest classification accuracy is equal to 0.992 across all evaluation metric methods. The best set of SEs is additionally combined with a decision tree classifier, which slightly improves ACC to 0.994.

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Section: Introductionmentioning

confidence: 99%