Machine Learning in Biotechnology: Current Applications and Future Prospects

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Advancements in biotechnology increasingly depend on the extensive utilization of big data, which is generated by modern high-throughout instrumentation technologies and is stored across numerous databases, both public and private. By applying biological sciences to economic and technological aims, biotechnology fosters beneficial endeavors for humanity. AI and its subset, machine learning (ML), demonstrate multifaceted utilities across various sectors, notably catalyzing advancements in biological research and healthcare. When biotechnology and AI progress symbiotically, they unlock exceptional potentials, aligning with Sustainable Development Goals and addressing myriad global challenges. ML's transformative impact on biological research continues to yield novel innovations in medicine and biotechnology. This article provides insight into the relationship between big data, biotechnology and extensive associated technologies such as artificial intelligence and machine learning. It explains how data integration, exploitation, and process optimization constitute three pivotal stages in any forthcoming biotechnology endeavor. Additionally, the article outlines several application areas of biotechnology where proficiency in utilizing big data will emerge as a crucial determinant, including gene editing and synthetic biology, predictive toxicology, drug discovery, drug repurposing, drug safety assessment, functional and structural genomics, bioprocess optimization, disease diagnosis and prognosis, pharmacogenomics and others.