Machine‐learning Methods for Predicting Gene Function, Protein Structure and Genomic Variation effects in Precision Biology
الموضوعات : Biotechnological Journal of Environmental Microbiology
Abdul Razak Mohamed Sikkander
1
,
Joel J. P. C. Rodrigues
2
,
Hala S. Abuelmakarem
3
,
Manoharan Meena
4
1 - Department of Chemistry, Velammal Engineering College, Chennai -600066 Tamilnadu INDIA
2 - National Institute of Telecommunications (Inatel), Santa Rita do Sapuca´ı, MG, Brazil; Instituto de Telecomunica¸c˜oes, Portugal; Federal University of Piau´ı (UFPI), Teresina, PI, Brazil
3 - Department of Biomedical Engineering, College of Engineering, King Faisal University, Al-Ahsa, 31982, Saudi Arabia
4 - Department of Chemistry, R.M.K. Engineering College, Kavaraipettai, Chennai-India
الکلمات المفتاحية: Machine learning, gene function prediction, protein structure, genomic variation, deep learning, bioinformatics, functional genomics,
ملخص المقالة :
Machine learning (ML) has emerged as a transformative approach in computational genomics, offering powerful tools to predict gene function, model protein structure, and identify genomic variations with unprecedented accuracy. Traditional bioinformatics methods, though effective, often struggle with the massive dimensionality and non-linear relationships inherent in genomic datasets. ML algorithms—such as random forests, support vector machines, convolutional and transformer neural networks—can learn complex representations from heterogeneous biological data, enabling functional annotation of uncharacterized genes, accurate modeling of protein folding, and detection of pathogenic variants. This paper explores the methodologies, results, and implications of integrating ML models in genomics and proteomics. A hypothetical dataset is presented to illustrate gene–function prediction, protein-structure inference, and variant classification using supervised and deep-learning frameworks. Results indicate that ML approaches can significantly outperform conventional statistical pipelines in prediction accuracy, generalization, and scalability. However, interpretability, data imbalance, and transferability across species remain major challenges. The discussion emphasizes the synergistic integration of ML with experimental validation, while future perspectives highlight the potential of foundation models and multimodal learning for functional genomics. Collectively, these advances bring us closer to a predictive, data-driven understanding of life’s molecular machinery.
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