Season 2025, Episode 116: Identifying Alzheimers Biomarkers Through Machine Learning | Advancements in Proteomics and Data Accessibility
In this episode of SciBud, we dive into a groundbreaking study exploring the intersection of Alzheimer's disease and artificial intelligence, revealing how machine learning can uncover potential biomarkers for early detection. Host Rowan takes us through the research process, which involved analyzing cerebrospinal fluid samples from patients with idiopathic normal pressure hydrocephalus—a condition that shares symptoms with Alzheimer’s but has its own unique pathology. By utilizing advanced techniques like the Synthetic Minority Over-sampling Technique and various machine learning models, researchers were able to identify promising protein markers, such as FABP3 and GOT1, that could enhance diagnostic capabilities. While the study shines a light on the potential of AI in medical research, it also opens up important discussions about data accessibility and methodological rigor. Join us as we unpack these exciting findings and their implications for battling Alzheimer’s, highlighting the vital role of innovative solutions in the fight against neurodegenerative diseases. Stay curious with SciBud, your trusted companion in the world of science!