Paper Info
Title
Disease-Genes Must Guide Data Source Integration in the Gene Prioritization Process.
Abstract
One of the main issues in detecting the genes involved in the etiology of genetic human diseases is the integration of different types of available functional relationships between genes. Numerous approaches exploited the complementary evidence coded in heterogeneous sources of data to prioritize disease-genes, such as functional profiles or expression quantitative trait loci, but none of them to our knowledge posed the scarcity of known disease-genes as a feature of their integration methodology. Nevertheless, in contexts where data are unbalanced, that is, where one class is largely under-represented, imbalance-unaware approaches may suffer a strong decrease in performance. We claim that imbalance-aware integration is a key requirement for boosting performance of gene prioritization (GP) methods. To support our claim, we propose an imbalance-aware integration algorithm for the GP problem, and we compare it on benchmark data with other state-of-the-art integration methodologies.
Year
DOI
Venue
2017
10.1007/978-3-030-14160-8_7
CIBB
Field
DocType
Citations 
Data source,Disease,Gene,Scarcity,Integration algorithm,Computer science,Prioritization,Artificial intelligence,Boosting (machine learning),Expression quantitative trait loci,Machine learning
Conference
0
PageRank 
References 
Authors
0.34
8
7
Name
Order
Citations
PageRank
Marco Frasca1389.72
Jean-Fred Fontaine21699.23
Giorgio Valentini390556.70
Marco Mesiti483072.53
Marco Notaro501.69
Dario Malchiodi66918.79
Miguel A Andrade-Navarro722015.60