Breast cancer prediction from microRNA profiling using random subspace ensemble of LDA classifiers via Bayesian optimization.

Breast cancer rates are rising. It also remains the second principal reason for cancer-related mortality in females, and the mortality rate is also drastically rising. In recent years, MicroRNAs (miRNAs) have emerged to have a large potential as biomarkers because of their effective roles in human disease diagnosis (including breast cancer). miRNAs are small (short), regulatory, and evolutionarily conserved non-coding RNAs (ncRNAs) molecules (with a length of about 22 nucleotides) that are present in all eukaryotic cells. There are many studies available in the literature that focus on recent circulating miRNAs research, their relationships to human diseases, their role as a potential biomarker, etc. Therefore, in this study we used three key techniques for classification of breast cancer using miRNAs features: Linear Discriminant Analysis (LDA), Random Subspace Ensemble (RSE) and Bayesian Hyperparameter optimization (BHO). Linear Discriminant Analysis (LDA) is a simple but most practical and computationally attractive classification approach. Random Subspace Ensemble (RSE) is capable of producing a robust ensemble for classification. Some previous research showed applications of Bayesian optimization in many engineering optimization problems. Notably, it is a recently applied for hyperparameter tuning in various ensemble classifiers. Therefore, the potential application of the RSE of LDA classifiers (LDA as a base classifier) with BHO method to boost the predicting accuracy of breast cancer diagnosis using miRNAs profiling dataset, has been studied in this study. A publicly available dataset of serum miRNA profiles obtained from the GEO dataset (accession code GSE106817) has been applied for validation. A variety of output measurements were employed to determine the performances and efficiencies of the proposed model and other classifiers. The proposed approach exhibited successful overall performance. The results were directly compared with the individual LDA classifier and other established state-of-the-art classifiers. The outcomes point out that the approach is superior in terms of different efficiency indicators to the LDA and all established state-of-the-art models used in the study. Study simulations, outcomes, and mathematical investigations have illustrated that the technique presented is a practical and advantageous model for the classification of breast cancer from miRNA profiling. This model may usefully be employed in other cancer classifications from miRNA profiling. [ABSTRACT FROM AUTHOR]