Marinka Zitnik and Vuk Janjic and Chris Larminie and Blaz Zupan and Natasa Przulj (2013) Discovering disease-disease associations by fusing systems-level molecular data. Scientific Reports, 13 . p. 3202.
Abstract
The advent of genome-scale genetic and genomic studies allows new insight into disease classification. Recently, a shift was made from linking diseases simply based on their shared genes towards systems-level integration of molecular data. Here, we aim to find relationships between diseases based on evidence from fusing all available molecular interaction and ontology data. We propose a multi-level hierarchy of disease classes that significantly overlaps with existing disease classification. In it, we find 14 disease-disease associations currently not present in Disease Ontology and provide evidence for their relationships through comorbidity data and literature curation. Interestingly, even though the number of known human genetic interactions is currently very small, we find they are the most important predictor of a link between diseases. Finally, we show that omission of any one of the included data sources reduces prediction quality, further highlighting the importance in the paradigm shift towards systems-level data fusion.
Item Type: | Article |
Keywords: | Machine Learning; Data Fusion; Disease Associations; Predictive medicine |
Related URLs: | |
Institution: | University of Ljubljana |
Department: | Faculty of Computer and Information Science |
Divisions: | Faculty of Computer and Information Science > Bioinformatics Laboratory |
Item ID: | 2285 |
Date Deposited: | 15 Nov 2013 17:07 |
Last Modified: | 15 Mar 2014 14:22 |
URI: | http://eprints.fri.uni-lj.si/id/eprint/2285 |
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