Neural signals naturally control most of the body’s organs and processes and are central to the presentation, progression and management of many chronic diseases. Decoding these neural signals into neural biomarkers provides an opportunity for both deeper understanding of chronic disease progression and serves as a target for therapy development.

 

By combining unique large neural datasets and machine learning, BIOS is able to learn neural biomarkers directly from the data. This data-driven method is many orders of magnitude faster, cheaper and more effective than conventional lab-based research.

 

In this whitepaper we set out the process of data collection and neural biomarker discovery for hypertension and how this can be repeated for other diseases.