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Article Abstract
The field of computational biology and bioinformatics has seen remarkable progress in recent years, driven largely by advancements in artificial intelligence (AI) technologies. This review synthesizes the latest developments in AI methodologies and their applications in addressing key challenges within the field of computational biology and bioinformatics. This review begins by outlining fundamental concepts in AI relevant to computational biology, including machine learning algorithms such as neural networks, support vector machines, and decision trees. It then explores how these algorithms have been adapted and optimized for specific tasks in bioinformatics, such as sequence analysis, protein structure prediction, and drug discovery. AI techniques can be integrated with big data analytics, cloud computing, and high-performance computing to handle the vast amounts of biological data generated by modern experimental techniques. The chapter discusses the role of AI in processing and interpreting various types of biological data, including genomic sequences, protein-protein interactions, and gene expression profiles. This chapter highlights recent breakthroughs in AI-driven precision medicine, personalized genomics, and systems biology, showcasing how AI algorithms are revolutionizing our understanding of complex biological systems and driving innovations in healthcare and biotechnology. Additionally, it addresses emerging challenges and future directions in the field, such as the ethical implications of AI in healthcare, the need for robust validation and reproducibility of AI models, and the importance of interdisciplinary collaboration between computer scientists, biologists, and clinicians. In conclusion, this comprehensive review provides insights into the transformative potential of AI in computational biology and bioinformatics, offering a roadmap for future research and development in this rapidly evolving field.
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