International Journal of Science, Engineering and TechnologyAuthors: Tetiana Hryhorivna Radchenko, Volodymyr Oleksandrovych Dovzhenko, Halyna Vasylivna Kononenko, Oleksii Serhiiovych Marchenko
Abstract: Bioinformatics pipelines play a critical role in processing large-scale biological datasets, often orchestrated using Unix shell scripts for modularity and efficiency. However, traditional scripting lacks the dynamic adaptability required to handle system failures, resource bottlenecks, and complex runtime anomalies. This paper explores the integration of artificial intelligence (AI) techniques into shell-based frameworks to enhance monitoring, error prediction, and optimization of bioinformatics workflows. Through intelligent anomaly detection, resource forecasting, and self-healing mechanisms, AI-augmented systems can significantly improve pipeline robustness. Case studies from genomics and metagenomics highlight improvements in computational efficiency and reduction in failure rates. The paper concludes by discussing implementation challenges and future directions for adaptive bioinformatics automation.
DOI: https://doi.org/10.5281/zenodo.16313879
