Transformer Neural Networks, Information Biology, and Alzheimer’s Disease

Abstract

AbstractBackgroundAlzheimer’s disease affects one in ten people older than 65 years, with the prevalence expected to triple by 2030, together with commensurate increases in cost. A comprehensive causal model of Alzheimer’s disease etiology is lacking despite more than three decades of study. Modern computational methods provide a new approach to multiscalar biosystems by modeling neuropathology in its native three‐dimensional character in topological biophysics and quantum computational models.MethodThis work describes how an emerging standard in machine learning, responsible for the AlphaFold (protein folding structure) and Gato generalist agent projects, transformer neural networks, may be applied to the study of Alzheimer’s disease per proven results in bot text chat, image recognition and creation, video generation, software programming, and autonomy in robotics and driving. The self‐attention and self‐learning mechanisms of transformer neural networks are discussed in application to multifaceted Alzheimer’s disease data sets involving neuroimaging scans, whole human genome data, and blood and CSF biomarker data.ResultA machine learning‐based topological biophysics model of Alzheimer’s disease neuropathology hypothesis‐testing (misfolded proteins, gene expression, diabetes type‐3, and peptide immune system stimulation) is described.ConclusionThere is an opportunity to apply state‐of‐the‐art transformer neural network machine learning techniques to the automated analysis of large accruing data stores of Alzheimer’s information (imaging, genomic, proteomic, and self‐reported data) towards the causal understanding of the neuropathology.