Harnessing deep learning in bioinformatics- opportunities, challenges, and ethical considerations

Abstract

The last few decades have seen a massive rise in the amount of biomedical data, which has pushed the use of various Machine Learning (ML) approaches to solve new issues in clinical research and biological science. Artificial intelligence (AI) is revolutionizing bioinformatics by enabling the rapid analysis of complex and enormous biological data, the identification of hidden patterns, and the development of prediction models for numerous biological databases. ML and Deep Learning (DL) techniques make it possible to automatically extract features, choose which ones to utilize, and create predictive models, which makes it possible to research complicated biological systems effectively. This study intends to present an overview of DL so that bioinformaticians using these models can evaluate all relevant technical and ethical issues. The findings from this study will encourage people to use DL techniques to resolve their research questions while taking accountability, explainability, fairness, and potential biases. Finally, this study examines the changing environment of AI-driven tools and algorithms, emphasizing their critical role in accelerating research, improving data interpretation, and catalyzing discoveries in biomedical sciences.

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