Phuong Nguyen

MeSH headings : Alzheimer Disease / metabolism; Alzheimer Disease / pathology; Amyloid / chemistry; Amyloid / metabolism; Amyloid beta-Peptides / chemistry; Amyloid beta-Peptides / metabolism; Amyotrophic Lateral Sclerosis / genetics; Amyotrophic Lateral Sclerosis / metabolism; Amyotrophic Lateral Sclerosis / pathology; Animals; Diabetes Mellitus, Type 2 / metabolism; Diabetes Mellitus, Type 2 / pathology; Humans; Islet Amyloid Polypeptide / chemistry; Islet Amyloid Polypeptide / metabolism; Models, Molecular; Neurodegenerative Diseases / metabolism; Neurodegenerative Diseases / pathology; Parkinson Disease / metabolism; Parkinson Disease / pathology; Protein Aggregation, Pathological; Proteostasis Deficiencies / metabolism; Superoxide Dismutase-1 / chemistry; Superoxide Dismutase-1 / metabolism; alpha-Synuclein / chemistry; alpha-Synuclein / metabolism; tau Proteins / chemistry; tau Proteins / metabolism

TL;DR: What computer, in vitro, in vivo, and pharmacological experiments tell us about the accumulation and deposition of the oligomers of the (Aβ, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer's disease, Parkinson's disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, respectively, are reviewed. (via Semantic Scholar)

author keywords: cancer heterogeneity; mathematical oncology; tumor growth; glioblastoma multiforme; virtual populations; nonlinear mixed effects; spatiotemporal data; Bayesian estimation; generative; adversarial networks; non-parametric estimation; variational autoencoders; machine learning

MeSH headings : Bayes Theorem; Humans; Machine Learning; Models, Theoretical; Neoplasms; Precision Medicine

TL;DR: Several techniques that can be used to aid the mathematical modeller in inferring and quantifying both sources of heterogeneity from patient data are reviewed, including virtual populations, nonlinear mixed effects modeling, non-parametric estimation, Bayesian techniques, and machine learning. (via Semantic Scholar)