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Admin / January 24, 2023

The concept of Artificial Intelligence is considered an old one; for many years it has been developing and evolving. Many scientists tapped the idea in different ways, across various branches of modern science; from mimicking the human brain to modern computers with microchips. AI became a reality at the dawn of the 21stcentury when it was applied successfully in applications. In biology, AI has developed from symbolic approaches to coded languages enabling machines to do complex operations. Recent advances done in AI are due to Deep Learning. It allowed computer assisted discovery to predict protein structure, as well as molecular design and drug discovery.

The human brain is limited when it comes to data collection or data integration. Without overcoming these limitations, reintegration would be impossible to achieve.  When key biological systems are stated differently, at all levels of biological organization, are too convoluted for the human brain to comprehend, therefore making human-driven reintegration impossible. The advancement of Artificial Intelligence methodologies on the other hand, is to be considered the best hope for overcoming those limitations. Reintegrating biology acknowledges the huge potential which existing AI methodologies have; as to accelerate researches, biological ones. Methodologies of AI and ML which already exist have proven to impact biological researches in a great way.

Biological disciplines which are based on AI-driven reintegration will launch a kind of biology. This newer kind will give us a chance to answer deep biological questions, which are currently found to be impossible to be answered.  Those questions are ranging from biological sub-disciplines, to the integration of the scales of all biological inquiries; including spatial, temporal as well as organizational.
AI will revolutionize biology with AI models are developed and that are more suited to biological applications rather than the existing machine learning techniques and current AI techniques. Advances and development require collaborations between both biological and computational scientists.