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Full-Text Articles in Medicine and Health Sciences
A Conformation Variant Of P53 Combined With Machine Learning Identifies Alzheimer Disease In Preclinical And Prodromal Stages, Giulia Abate, Marika Vezzoli, Letizia Polito, Antonio Guaita, Diego Albani, Moira Marizzoni, Emirena Garrafa, Alessandra Marengoni, Gianluigi Forloni, Giovanni B. Frisoni, Jeffrey L. Cummings, Maurizio Memo, Daniela Uberti
A Conformation Variant Of P53 Combined With Machine Learning Identifies Alzheimer Disease In Preclinical And Prodromal Stages, Giulia Abate, Marika Vezzoli, Letizia Polito, Antonio Guaita, Diego Albani, Moira Marizzoni, Emirena Garrafa, Alessandra Marengoni, Gianluigi Forloni, Giovanni B. Frisoni, Jeffrey L. Cummings, Maurizio Memo, Daniela Uberti
School of Medicine Faculty Publications
© 2020 by the authors. Li-censee MDPI, Basel, Switzerland. Early diagnosis of Alzheimer’s disease (AD) is a crucial starting point in disease man-agement. Blood-based biomarkers could represent a considerable advantage in providing AD-risk information in primary care settings. Here, we report new data for a relatively unknown blood-based biomarker that holds promise for AD diagnosis. We evaluate a p53-misfolding conformation rec-ognized by the antibody 2D3A8, also named Unfolded p53 (U-p532D3A8+), in 375 plasma samples derived from InveCe.Ab and PharmaCog/E-ADNI longitudinal studies. A machine learning approach is used to combine U-p532D3A8+ plasma levels with Mini-Mental State Examination (MMSE) and apolipoprotein E …
Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han
Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han
Public Health Faculty Publications
The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures …