MTGO: PPI Network Analysis Via Topological and Functional Module Identification
Di Silvestre, Dario
AffiliationUniv Arizona Hlth Sci, Inst Ctr Biomed Informat & Biostat BIO5
MetadataShow full item record
PublisherNATURE PUBLISHING GROUP
CitationVella, D., Marini, S., Vitali, F., Silvestre, D., Mauri, G., & Bellazzi, R. (2018). MTGO: PPI Network Analysis Via Topological and Functional Module Identification. Scientific reports, 8(1), 5499.
Rights© The Author(s) 2018. This article is licensed under a Creative Commons Attribution 4.0 International License.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractProtein-protein interaction (PPI) networks are viable tools to understand cell functions, disease machinery, and drug design/repositioning. Interpreting a PPI, however, it is a particularly challenging task because of network complexity. Several algorithms have been proposed for an automatic PPI interpretation, at first by solely considering the network topology, and later by integrating Gene Ontology (GO) terms as node similarity attributes. Here we present MTGO - Module detection via Topological information and GO knowledge, a novel functional module identification approach. MTGO let emerge the bimolecular machinery underpinning PPI networks by leveraging on both biological knowledge and topological properties. In particular, it directly exploits GO terms during the module assembling process, and labels each module with its best fit GO term, easing its functional interpretation. MTGO shows largely better results than other state of the art algorithms (including recent GO-based ones) when searching for small or sparse functional modules, while providing comparable or better results all other cases. MTGO correctly identifies molecular complexes and literature-consistent processes in an experimentally derived PPI network of Myocardial infarction. A software version of MTGO is available freely for non-commercial purposes at https://gitlab.com/d1vella/MTGO.
NoteOpen access journal.
VersionFinal published version
SponsorsGenomic profiling of rare hematologic malignancies, development of personalized medicine strategies, and their implementation into the Rete Ematologica Lombarda (REL) clinical network project; Inherited arrhythmias, clinical characterization, genetic geography and experimental studies in the Calabria Region Isolate project
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