The University of Wisconsin-Madison (UW)'s Daifeng Wang and colleagues used machine learning (ML) and artificial intelligence to better understand how interacting traits influence brain cells' functions.
The researchers applied manifold learning to predict neuronal features by aligning gene expression and electrophysiological data for about 3,000 neurons in the mouse brain; both neuronal cell features manifested high values in the same group of cells, but low values in the remainder, and exhibited a relationship to one another that described their manifold shape.
The researchers then used cell clusters to unveil connections between electrophysiological features and specific genes governing the expression of other genes. This informed the development of deepManReg, a new manifold learning model that enhances the prediction of neuronal traits based on gene expression and electrophysiology. "Basically, [we can study] how those genes are regulated to affect the electrophysiology or behaviors in diseased cells," Wang said.
From University of Wisconsin-Madison News=
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