Matteo Barbieri
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Matteo Barbieri has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Alessandro Verri
DIBRIS, University of Genoa, via Dodecaneso 35, I-16146 Genova, Italy
1 shared publication - Annalisa Barla
Università di Genova
1 shared publication - Margherita Squillario
IRCCS Ospedale Policlinico San Martino, 16131 Genova, Italy
1 shared publication
Latest Publications
- Open Access
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.
Open AccessEnhancing 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.