| Sumario: | Simple Summary: Breast cancer is a non-homogeneous disease and consists of diverse molecular subtypes that vary based on their prognosis and treatment response. Subtype classification of breast cancer is a crucial step toward improving diagnostic efficiency and personalized treatment. MicroRNAs (miRNAs) are a group of small regulatory RNAs that have been shown to possess great promise as a biomarker for classifying cancers. But analyzing data related to miRNAs is a challenge due to the complexities involved. In this work, we proposed an optimization algorithm dubbed the Adaptive Hill Climbing Artificial Lemming Algorithm (AHALA), which is designed to improve miRNA subtyping in breast cancers. Our proposed algorithm combined feature selection based on biological knowledge with the use of a machine learning algorithm in order to uncover the important miRNAs. Using publicly available datasets of breast cancers, the proposed algorithm showed promising results in distinguishing subtypes of breast cancers, as well as identifying important miRNAs as breast cancer subtyping biomarkers. Background: Breast cancers are heterogeneous in nature, including many molecular subtypes, each displaying varying characteristics in clinical outcomes as well as in responses to treatments. Subtyping requires absolute precision for the application of precision medicine; however, this is not an easy task, given the dimensionality as well as noise in miRNA expression profiles. Even though miRNAs display potential as a biological marker for subtyping breast cancers, feature selection and optimizing learning algorithms would help harness their potential as a diagnostic tool. Methods: We propose the Adaptive Hill Climbing Artificial Lemming Algorithm (AHALA), a hybrid optimization framework that integrates the global search capability of the Artificial Lemming Algorithm with an adaptive hill-climbing local search strategy. Low-variance filtering and differential gene expression analysis were first applied to reduce dimensionality and enhance biological relevance. AHALA was then used to optimize deep neural network hyperparameters for miRNA-based multi-class breast cancer subtype classification. The method was validated using TCGA breast cancer miRNA expression data and benchmarked against state-of-the-art optimization algorithms using the CEC2021 test suite. Results: AHALA had a high classification performance measure for each type of breast cancer with a mean accuracy of 95.74%, precision of 95.98%, recall of 95.74%, F1 measure of 95.74%, and AUC value of 0.9682. The new algorithm had superior convergence and significance compared with other optimization algorithms. Feature selection revealed miRNAs that belong to each subtype, such as hsa-miR-190b, hsa-miR-429, hsa-miR-505-3p, hsa-miR-3614-5p, and hsa-miR-935. Conclusions: The AHALA framework offers a potent and efficient method of performing miRNA-based subtyping of breast cancer that integrates global exploration and local search to its advantage. Its high level of classification, stability, and ability to identify biologically important biomarkers mark this method as promising.
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