Enhancing Interpretability of Gene Signatures with Prior Biological Knowledge.
Biological interpretability is a key requirement for the output of microarray data analysis pipelines. The most used pipeline first identifies a gene signature from the acquired measurements and then uses gene enrichment analysis as a tool for functionally characterizing the obtained results. Recently Knowledge Driven Variable Selection (KDVS), an alternative approach which performs both steps at the same time, has been proposed. In this paper, we assess the effectiveness of KDVS against standard approaches on a Parkinson's Disease (PD) dataset. The presented quantitative analysis is made possible by the construction of a reference list of genes and gene groups associated to PD. Our work shows that KDVS is much more effective than the standard approach in enhancing the interpretability of the obtained results.
DIBRIS, University of Genoa, Via Dodecaneso 35, I-16146 Genova, Italy. margherita.squillario@unige.it.
Margherita
M
Squillario
DIBRIS, University of Genoa, Via Dodecaneso 35, I-16146 Genova, Italy. matteo.barbieri@dibris.unige.it.
Matteo
M
Barbieri
DIBRIS, University of Genoa, Via Dodecaneso 35, I-16146 Genova, Italy. alessandro.verri@unige.it.
Alessandro
A
Verri
DIBRIS, University of Genoa, Via Dodecaneso 35, I-16146 Genova, Italy. annalisa.barla@unige.it.
Annalisa
A
Barla
KDVS; Parkinson’s disease; established domain knowledge; functional characterization; gene expression; gene ontology; sparse regularization; variable selection