A Belgian research team has utilized deep learning to investigate the genetic switches that define brain cell types across species, including humans, mice, and chickens. Their study reveals that while some cell types have remained conserved over millions of years, others have evolved distinct characteristics. This research not only enhances understanding of brain evolution but also offers tools for studying gene regulation in various contexts, including disease states.
The team, led by Prof. Stein Aerts at VIB.AI, developed machine learning models to analyze the regulatory codes of brain cells, revealing significant insights into the similarities and differences between avian and mammalian brains. Their findings indicate that certain regulatory codes for bird neurons are akin to those found in mammalian deep-layer neurons, highlighting the evolutionary connections. This research paves the way for further exploration of genetic variations linked to diseases like Parkinson's, showcasing the potential of AI in both evolutionary biology and medical research.
• Deep learning models reveal conserved and divergent brain cell regulatory codes.
• Research connects genetic switches to brain evolution across species.
Deep learning models analyze complex data to identify patterns in gene regulation across species.
The regulatory code consists of genetic switches that control gene activity in different cell types.
Machine learning models are employed to characterize and compare brain cell types over evolutionary time.
VIB is a research institute focused on life sciences, utilizing AI to advance understanding of brain and disease research.
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