A Multivariate Biomarker for Parkinson's Disease
This addresses the need for reliable biomarkers in Parkinson's disease diagnosis, but it appears incremental as it builds on existing genomic analysis with a multivariate approach.
The study tackled the problem of detecting and classifying Parkinson's disease samples by identifying a multivariate biomarker, resulting in a group of 20 genes that showed clear potential for accurate classification even in the presence of other neurodegenerative disorders.
In this study, we executed a genomic analysis with the objective of selecting a set of genes (possibly small) that would help in the detection and classification of samples from patients affected by Parkinson Disease. We performed a complete data analysis and during the exploratory phase, we selected a list of differentially expressed genes. Despite their association with the diseased state, we could not use them as a biomarker tool. Therefore, our research was extended to include a multivariate analysis approach resulting in the identification and selection of a group of 20 genes that showed a clear potential in detecting and correctly classify Parkinson Disease samples even in the presence of other neurodegenerative disorders.