Machine Learning-Driven Evolutionary Fuzzy Clustering for High-Dimensional Genomic Data Analysis
DOI:
https://doi.org/10.46610/JoFSFLD.2025.v02i03.002Keywords:
Biomarker discovery, Evolutionary fuzzy clustering, Genomic data analysis, High-dimensional data, Machine learning, Precision medicineAbstract
Clustering and classification Genomic data analysis is a fundamental part of contemporary bioinformatics and precision medicine, yet clustering and classification of high-dimensional genomic data is extremely challenging due to noise and uncertainty. The traditional methods of clustering (such as hard partitioning) techniques are not practical in biological data that display overlapping cluster structures, and existing fuzzy clustering methods (such as Fuzzy C-Means (FCM)) are sensitive to initialisation and are likely to converge on local optima. This paper presents a high-dimensional genomic-specific Machine Learning-based Evolutionary Fuzzy Clustering (MLEFC) architecture to ensure these challenges are addressed. The suggested framework makes the power of the evolutionary algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to maximize centres of fuzzy clusters and membership degrees to address the weakness of traditional clustering. Also, machine learning models are incorporated on cluster validation, predictive analysis, and enhanced biological interpretability. This framework is tested with benchmark genomic data, and results show that it has significantly better clustering accuracy, stability and scale than the traditional ones. Additionally, the biological validation underscores the potential of the framework in finding functional groups of genes, disease subtypes and candidate biomarkers. MLEFC introduces a powerful and interpretive tool to genomic data mining by incorporating fuzzy logic, evolutionary computing, and machine learning and has direct applications to biomarker discovery, cancer subtype classification, and personalized medicine.
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