Efficient Detection of Ion Channel Switching through Rapid Random Forest Models

Abstract

The proper function of ion channels is fundamental to many of our physiological processes, such as neural signal transmission, muscle contraction, and insulin secretion. As a result of genetic mutations and environmental factors, however, the dysfunction of ion channels can impede crucial cellular processes, resulting in channelopathies, such as cystic fibrosis and hypoglycemia, which may lead to highly impaired respiratory, cardiovascular, and muscular function. To detect ion channel dysfunction, a GPU-accelerated random forest model with an F1-score of 0.935 was developed to efficiently analyze electrophysiological time series data for the improper opening and closing of ion channels. The random forest model can run 200x faster than real-time 10 kHz electrophysiological data collection, holding immense potential for the development of reactive clinical measures for ion channel dysfunction and the development of novel therapies for various ion channel-based musculoskeletal disorders.