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.
@article{arxiv.1602.07264,
title = {A Multivariate Biomarker for Parkinson's Disease},
author = {Giancarlo Crocetti and Michael Coakley and Phil Dressner and Wanda Kellum and Tamba Lamin},
journal= {arXiv preprint arXiv:1602.07264},
year = {2016}
}
Comments
5 pages, 4 figures, 3 tables, published at the Research Day at Pace University, New York. Proceedings of 12th Annual Research Day, 2014 - Pace University