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From the third issue of 2017, Microarrays has changed its name to High-Throughput.

Open AccessFeature PaperArticle
Microarrays 2016, 5(2), 15; doi:10.3390/microarrays5020015

Enhancing Interpretability of Gene Signatures with Prior Biological Knowledge

DIBRIS, University of Genoa, Via Dodecaneso 35, I-16146 Genova, Italy
These authors contributed equally to this work.
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Author to whom correspondence should be addressed.
Academic Editor: Heather J. Ruskin
Received: 5 October 2015 / Revised: 25 May 2016 / Accepted: 31 May 2016 / Published: 8 June 2016
(This article belongs to the Special Issue Computational Modeling and Analysis of Microarray Data: New Horizons)
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Abstract

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. View Full-Text
Keywords: gene expression; functional characterization; variable selection; sparse regularization; established domain knowledge; KDVS; Parkinson’s disease; gene ontology gene expression; functional characterization; variable selection; sparse regularization; established domain knowledge; KDVS; Parkinson’s disease; gene ontology
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Squillario, M.; Barbieri, M.; Verri, A.; Barla, A. Enhancing Interpretability of Gene Signatures with Prior Biological Knowledge. Microarrays 2016, 5, 15.

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