Artificial Intelligence in Bioinformatics: Cutting-Edge Techniques and Future Prospects

Abstract This research detailed the use of artificial intelligence (AI) in bioinformatics, current techniques for future trends, and an update on the most appropriate AI techniques for genomic data analysis, protein structure prediction, and modeling complex networks in biology. This review was conducted using multiple-step approach, including systematic surveys of existing literature on AI methodologies, and their applications, algorithmics comparison and case study investigations. Algorithmic comparison is made to evaluate the performance, accuracy, and computational cost of various AI models applied to bioinformatics problems. Similarly, domain-specific testing was performed using real bioinformatics case studies such as drug-target interaction predictions and the identification of cancer biomarkers. The findings from this review shows that in genomic data analysis, deep neural networks, including the use of convolutional neural networks (CNN) and recurrent neural networks (RNN), are found to be superior to conventional machine learning algorithms such as support vector machines (SVM), in variant calling and gene prediction. Comparative studies demonstrated that deep learning-based models, such as DeepVariant are more precise in the prediction of the single nucleotide polymorphism (SNP) than the standard statistical models, as sensitivity and specificity are enhanced. Wilcoxon signed-rank tests and cross-validation techniques were also used to provide stable performance comparison across different datasets and problem domains. The results of this research emphasize the transformative impact of AI on bioinformatics, demonstrating how various methodologies reinforce each other to improve the accuracy, speed, and interpretability of results in bioinformatics for future applications. It is recommended that future research should focus on integrating hybrid AI models uniting statistical, deep learning approaches with explainability methodologies for biomedical research and medical applications. Keywords: Artificial Intelligence, Data, Bioinformatics, Genome