Finding genes associated with human genetic disorders is one of the most challenging problems in bio-medicine. In this context, to guide researchers in detecting the most reliable candidate causative-genes for the disease of interest, gene prioritization methods represent a necessary support to automatically rank genes according to their involvement in the disease under study. This problem is characterized by highly unbalanced classes (few causative and much more non-causative genes) and requires the adoption of cost-sensitive techniques to achieve reliable solutions. In this work we propose a network-based methodology for disease-gene prioritization designed to expressly cope with the data imbalance. Its validation over a benchmark composed of 708 selected medical subject headings (MeSH) diseases, shows that our approach is competitive with state-of-art methodologies, and its reduced time complexity makes its application feasible on large-size datasets.
Gene-disease prioritization through cost-sensitive graph-based methodologies / M. Frasca, S. Bassis - In: Bioinformatics and biomedical engineering / [a cura di] F. Ortuño, I. Rojas. - Prima edizione. - [s.l] : Springer International, 2016. - ISBN 9783319317434. - pp. 739-751 (( Intervento presentato al 4. convegno International Work-Conference on Bioinformatics and Biomedical Engineering tenutosi a Granada nel 2016.
|Titolo:||Gene-disease prioritization through cost-sensitive graph-based methodologies|
FRASCA, MARCO (Primo)
BASSIS, SIMONE (Ultimo)
|Parole Chiave:||Gene-disease prioritization; graph-based node ranking; cost-sensitive learning|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||2016|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/978-3-319-31744-1_64|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|