Abstract
Background High-throughput (HT) data analysis can be used to characterize Parkinson's disease (PD), the second most widespread neurodegenerative disease in the world, at the functional level and to identify possible candidate biomarkers and relevant functions involved in the disease early stage. Materials and Methods To this aim, we compare the results of two different pipelines in the analysis of the same early PD dataset (GSE6613) [1]: •the Standard pipeline in which prior knowledge is used a posteriori, after the gene signature identification; •the Knowledge Driven Variable Selection (KDVS) [2] in which prior knowledge is used a priori to structure the data matrix before variable selection. In both pipelines variable selection is performed by L1L2FS [6], an embedded variable selection method based on regularization that incorporates feature selection within the classification step. Gene Ontology (GO) [11] is the source of prior knowledge for both . Results Each pipeline identified a gene signature and a list of GO terms. The results were compared via two different procedures, namely Literature characterization and Benchmark analysis. Both pipelines were able to identify relevant gene signatures and GO terms concerning PD. The results obtained by KDVS cover much more certified knowledge on PD with respect to the results from the Standard procedure.
Published Version
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