Machine Learning Identifies Age-Specific Hallmarks of Alzheimer
Combining datasets on gene expression, RNA sequencing, and proteomics shows age-dependent brain changes in Alzheimer’s
For bigger-picture ideas in science, philosophy, technology, psychology, and whatever else my chaotic brain takes an interest in, check out my newsletter Thinking Ahead. Your visit will be much appreciated.
Early diagnosis
Alzheimer’s disease (AD) starts decades before the symptoms become apparent. That is a problem. Diagnosis arrives always too late.
Scientists have made progress in understanding the condition and a lot of potential interventions are being studied, from gut microbes or probiotics, to immune therapy and small designer molecules. At the moment, though, lifestyle changes are the best and most customizable bets we have to safeguard our brains.
All the potential treatments, though, share one fatal flaw: too little, too late. This is why a very important subfield in AD research focuses on finding good predictive makers, perhaps in the form of specific blood proteins or changes in the eyes. Machine learning systems might also lend a hand by figuring out how to diagnose (early) dementia on MRI scans.
