A modular ensemble of 21 deep neural networks (DNNs) of varying depth, structure and optimization to predict human chronological age using a basic blood test. Aging.AI allows any patient with blood test data to predict their age and sex. The ensemble approach may facilitate integration of multi-modal data linked to chronological age and sex that may lead to simple, minimally invasive, and affordable methods of tracking integrated biomarkers of aging in humans and performing cross-species feature importance analysis.
Pharma.AI Department, Insilico Medicine, Inc, Baltimore, MD, USA; Computer Technologies Lab, ITMO University, St. Petersburg, Russia; The Biogerontology Research Foundation, Oxford, UK; School of Systems Biology, George Mason University (GMU), Fairfax, VA, USA; Invitro Laboratory, Ltd, Moscow, Russia; Department of Biomedical Engineering, Boston University, Boston, MA, USA; Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
Aging.AI funding source(s)
This work was financially supported by the Government of Russian Federation, Grant 074-U01 and by a research grant from the Life Extension Foundation 2016-LEF- AA-INSIL.