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Article ; Online: GLADIATOR

Yael Silberberg / Martin Kupiec / Roded Sharan

Genome Medicine, Vol 9, Iss 1, Pp 1-

a global approach for elucidating disease modules

2017 Volume 14

Abstract: ... named GLADIATOR (GLobal Approach for DIsease AssociaTed mOdule Reconstruction). GLADIATOR relies ... mechanisms. Methods We devised a global method for the prediction of multiple disease modules simultaneously ... suggesting novel proteins involved in its pathology. Conclusions GLADIATOR predicts disease modules ...

Abstract	Abstract 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 ...
Keywords	Disease gene prediction ; Disease modules ; Disease pathways ; Graphs and networks ; Protein-protein interaction network ; Hyperinsulinism ; Medicine ; R ; Genetics ; QH426-470
Subject code	006
Language	English
Publishing date	2017-05-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: GLADIATOR: a global approach for elucidating disease modules.

Silberberg, Yael / Kupiec, Martin / Sharan, Roded

Genome medicine

2017 Volume 9, Issue 1, Page(s) 48

Abstract: ... simultaneously named GLADIATOR (GLobal Approach for DIsease AssociaTed mOdule Reconstruction). GLADIATOR relies ... mechanisms.: Methods: We devised a global method for the prediction of multiple disease modules ... suggesting novel proteins involved in its pathology.: Conclusions: GLADIATOR predicts disease modules ...

Abstract	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 .
MeSH term(s)	Algorithms ; Computational Biology/methods ; Genetic Predisposition to Disease ; Humans ; Hyperinsulinism/diagnosis ; Hyperinsulinism/genetics ; Metabolic Networks and Pathways ; Molecular Sequence Annotation ; Protein Interaction Maps
Language	English
Publishing date	2017-05-26
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2484394-5
ISSN	1756-994X ; 1756-994X
ISSN (online)	1756-994X
ISSN	1756-994X
DOI	10.1186/s13073-017-0435-z
Database	MEDical Literature Analysis and Retrieval System OnLINE

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