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2017 ; 9
(1
): 48
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GLADIATOR: a global approach for elucidating disease modules
#MMPMID28549478
Silberberg Y
; Kupiec M
; Sharan R
Genome Med
2017[May]; 9
(1
): 48
PMID28549478
show ga
BACKGROUND: Understanding the genetic basis of disease is an important challenge
in biology and medicine. The observation that disease-related proteins often
interact with one another has motivated numerous network-based approaches for
deciphering disease mechanisms. In particular, protein-protein interaction
networks were successfully used to illuminate disease modules, i.e., interacting
proteins working in concert to drive a disease. The identification of these
modules can further our understanding of disease mechanisms. METHODS: We devised
a global method for the prediction of multiple disease modules simultaneously
named GLADIATOR (GLobal Approach for DIsease AssociaTed mOdule Reconstruction).
GLADIATOR relies on a gold-standard disease phenotypic similarity to obtain a
pan-disease view of the underlying modules. To traverse the search space of
potential disease modules, we applied a simulated annealing algorithm aimed at
maximizing the correlation between module similarity and the gold-standard
phenotypic similarity. Importantly, this optimization is employed over hundreds
of diseases simultaneously. RESULTS: GLADIATOR's predicted modules highly agree
with current knowledge about disease-related proteins. Furthermore, the modules
exhibit high coherence with respect to functional annotations and are highly
enriched with known curated pathways, outperforming previous methods. Examination
of the predicted proteins shared by similar diseases demonstrates the diverse
role of these proteins in mediating related processes across similar diseases.
Last, we provide a detailed analysis of the suggested molecular mechanism
predicted by GLADIATOR for hyperinsulinism, suggesting novel proteins involved in
its pathology. CONCLUSIONS: GLADIATOR predicts disease modules by integrating
knowledge of disease-related proteins and phenotypes across multiple diseases.
The predicted modules are functionally coherent and are more in line with current
biological knowledge compared to modules obtained using previous disease-centric
methods. The source code for GLADIATOR can be downloaded from
http://www.cs.tau.ac.il/~roded/GLADIATOR.zip .
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