Next Article in Journal
Evaluation of Solid Supports for Slide- and Well-Based Recombinant Antibody Microarrays
Next Article in Special Issue
Computational Modeling and Analysis of Microarray Data: New Horizons
Previous Article in Journal
Retrospective Proteomic Analysis of Cellular Immune Responses and Protective Correlates of p24 Vaccination in an HIV Elite Controller Using Antibody Arrays
Previous Article in Special Issue
Cancer Biomarkers from Genome-Scale DNA Methylation: Comparison of Evolutionary and Semantic Analysis Methods
Please note that this journal is no longer accepting submissions. All previous published papers will remain fully searchable on www.mdpi.com.
Article

Enhancing Interpretability of Gene Signatures with Prior Biological Knowledge

DIBRIS, University of Genoa, Via Dodecaneso 35, I-16146 Genova, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
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)
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
Show Figures

Graphical abstract

MDPI and ACS Style

Squillario, M.; Barbieri, M.; Verri, A.; Barla, A. Enhancing Interpretability of Gene Signatures with Prior Biological Knowledge. Microarrays 2016, 5, 15. https://0-doi-org.brum.beds.ac.uk/10.3390/microarrays5020015

AMA Style

Squillario M, Barbieri M, Verri A, Barla A. Enhancing Interpretability of Gene Signatures with Prior Biological Knowledge. Microarrays. 2016; 5(2):15. https://0-doi-org.brum.beds.ac.uk/10.3390/microarrays5020015

Chicago/Turabian Style

Squillario, Margherita, Matteo Barbieri, Alessandro Verri, and Annalisa Barla. 2016. "Enhancing Interpretability of Gene Signatures with Prior Biological Knowledge" Microarrays 5, no. 2: 15. https://0-doi-org.brum.beds.ac.uk/10.3390/microarrays5020015

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop