Machine learning artificial intelligence is a technology that has taken over the world and changed the way we think about life. While many now praise or condemn them, no one dismisses the fact that artificial intelligence and machine learning have several compelling benefits across a wide range of industries. In particular, they are revolutionizing the way biological research is conducted, leading to new innovations in healthcare and biotechnology.
The transformative power of machine learning is being used to analyze very large sets of biological data from next-generation sequencing, environmental imaging from satellites, drones, as well as light microscopy, and much more. AI provides the key to understanding very complex biological circuits.
Machine learning is improving our understanding of complex processes, such as aging. For example, in a new interdisciplinary project, scientists plan to use fission yeast as a genetic model of an organism along with multi-step machine learning to identify the biological processes that are fundamental to aging. AI will help learn more about living organisms, which includes the human body.
Researchers in the Department of Biological Sciences are using AI to understand where certain animal and plant species are distributed and how their biodiversity responds to environmental changes and contributes to their development and mutations. This uses state-of-the-art computational technologies, including geographic information systems, remote sensing, machine learning, and environmental modeling.
Another striking application of AI in biology is the study of chromosomal instability, which seeks to understand the mechanisms by which early somatic mutations and copy number changes disrupt cell cycle regulation and the mitotic apparatus, causing malignant tumor development. Understanding these mechanisms is an important step forward in the early diagnosis and prevention of cancer.
Biologists are also using machine learning techniques to accurately predict the three-dimensional shapes of drug targets and other important biological molecules, even when only limited data are available. And this solves the most difficult problem of modern biology and medical discovery.
The future prospects for AI in biology are very promising, as technology advances by leaps and bounds.