Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

The rapid growth of biological data generated through high-throughput sequencing technologies has created a strong need for intelligent computational techniques to analyze, interpret, and extract meaningful knowledge from complex datasets. Machine Learning (ML) has emerged as a powerful tool in bioinformatics and genomics for handling large-scale biological data, identifying patterns, predicting biological functions, and assisting in disease diagnosis. This paper reviews the role of ML in genomics and bioinformatics, discussing commonly used algorithms, data preprocessing techniques, and key application areas such as gene prediction, protein structure prediction, disease classification, and drug discovery. We also highlight challenges like data imbalance, dimensionality, and interpretability issues. Recent advancements including deep learning and hybrid approaches are also discussed. This review provides an overview for researchers interested in applying ML to biological datasets and shows how computational intelligence is transforming modern biology.

Keywords: Machine Learning, Bioinformatics, Genomics, Gene Prediction, Deep Learning, Protein Structure, Disease Classification