Diagnostic AI Modeling and Pseudo Time Series Profiling of AD and PD Based on Individualized Serum Proteome Data - Zhang, Jianhu; Zhang, Xiuli; Sh, Yuan; Liu, Benliang; Hu, Zhiyuan (Frontiers in Bioinformatics, 2021)
Background Parkinson's disease (PD), Alzhaimer's disease (AD) are common neurodegenerative disease, and mild cognitive impairment (MCI) may be happened in the early stage of AD or PD. Blood biomarkers are considered to be less invasive, less cost and more convenient, and there is tremendous potentia for the diagnosis and prediction of neurodegenerative diseases. As a recently mentioned field, artificial intelligence (AI) is often applyed in biology and shows excellent results. Method Human blood protein microarray profiles including 156 CT, 50 MCI, 132 PD, 50 AD samples are collected from Gene Expression Omnibus (GEO). First, we used bioinformatics methods and feature engineering in machine learning to screen important features, constructed ANNclassifier models based on these features to distinguish samples, and evaluated the model's performance with classification accuracy and Area Under Curve (AUC). Secenod, we used Ingenuity Pathway Anaylsis (IPA) methods to analyse the pathways and functions in early stage and late stage samples of different diseases, and potential targets for drug intervention by predicting upstream regulators. Result Overall, we incorporated six indicators, including EPHA2, MRPL19, SGK2, to build a diagnostic model for AD with a test set accuracy of up to 98.07%. Incorporating 15 indicators such as ERO1LB, FAM73B, IL1RN to build a diagnostic model for PD, with a test set accuracy of 97.05%. Thirty indicators such as XG, FGFR3 and CDC37 were incorporated to establish a four-category diagnostic model for both AD and PD, with a test set accuracy of 98.71%. In addition,we found that early PD may occur earlier than early MCI. Finally, there are 24 proteins that may serve as potential therapeutic targets