Blood biomarker-based classification study for neurodegenerative diseases

As the population ages, neurodegenerative diseases (NDs) are becoming more prevalent, making it crucial to comprehend the underlying disease mechanisms and identify biomarkers to allow for early diagnosis and effective screening for clinical trials. Thanks to advancements in gene expression profiling, it is now possible to search for disease biomarkers on an unprecedented scale. Here we applied machine learning (ML) approaches to identify blood-based biomarkers for Alzheimer’s (AD) and Parkinson’s disease (PD) with the application of multiple feature selection methods. In addition to traditional ML approaches, deep learning (DL) algorithms were also utilized for comparison. One optimal random forest (RF) model was discovered for AD with 159 gene markers (ROC-AUC = 0.886) while one optimal RF model was discovered for PD (ROC-AUC = 0.743). The convolutional neural network (CNN) performs consistently well across both AD and PD, suggesting its potential in biomarker identification and disease detection.