A Comprehensive Review of Novel AI Techniques and Applications in Bioinformatics

Abstract

The integration of Artificial Intelligence (AI) models into bioinformatics opens new avenues in biological data analysis and interpretation. The current study follows PRISMA guidelines for the search strategy, and the databases covered include PubMed, Embase, and Google Scholar for keywords that focus on studies published between 2017 and 2024. As for the aimed bioinformatics domains, we explored the uses of Al methodologies such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) in the broad bioinformatics field. Other applications of this paradigm are genome sequence analysis, 2D/3D protein structure folding and prediction, systems biology, customized medicine for individuals, drug discovery, medical image analysis, signals and pathways processing, clinical data analysis, and biomedical text mining. AI systems have demonstrated spectacular effectiveness in addressing complex biological problems, from drug development to personalized medicine, protein structure prediction, and protein folding. In summary, this paper examines the rapidly changing field of AI tools and algorithms and their integration with bioinformatics. It highlights their critical function in accelerating biomedical research, simplifying data interpretation, and stimulating advancements.