The Impact of Machine Learning and AI on Bioinformatics
DOI:
https://doi.org/10.54097/7kn89v49Keywords:
Bioinformatics, Machine Learning, Artificial Intelligence, Genomics, Multi-Omics Integration, Disease Prediction, Deep Learning, Explainable AI, Precision Medicine.Abstract
The rapid growth of biological data has fundamentally transformed bioinformatics, challenging the effectiveness of traditional methods. This paper examines the evolving role of AI and ML in addressing the limitations of conventional methods such as HMMs and sequence alignment tools. Unlike rule-based approaches, ML techniques enable scalable, data-driven analysis capable of capturing complicated and nonlinear relationships within genomic and multi-omics data. Key applications, including genomic sequence classification, disease prediction, and multi-omics integration, demonstrates the significant advantage of machine-driven methods in improving predictive accuracy and expanding analytical scope. However, challenges such as model interpretability and computational demands remain critical concerns, prompting the development of AI frameworks that are explainable. This paper argues that rather than replacing traditional methods, AI and ML should serve as complementary tools that enhance the overall analytical framework. Future directions highlight the potential of generative models and decision-based machine systems to further advance biological discovery and precision medicine.
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