Abstract

Correlation networks are emerging as a powerful tool for modeling temporal mechanisms within the cell. Particularly useful in examining coexpression within microarray data, studies have determined that correlation networks follow a power law degree distribution and thus manifest properties such as the existence of "hub" nodes and semicliques that potentially correspond to critical cellular structures. Difficulty lies in filtering coincidental relationships from causative structures in these large, noise-heavy networks. As such, computational expenses and algorithm availability limit accurate comparison, making it difficult to identify changes between networks. In this vein, we present our work identifying temporal relationships from microarray data obtained from mice in three stages of life. We examine the characteristics of mouse networks, including correlation and node degree distributions. Further, we identify high degree nodes ("hubs") within networks and define their essentiality. Finally, we associate Gene Ontology annotations to network structures to deduce relationships between structure and cellular functions.

Original languageEnglish (US)
Title of host publicationProceedings of the 44th Annual Hawaii International Conference on System Sciences, HICSS-44 2010
DOIs
StatePublished - 2011
Event44th Hawaii International Conference on System Sciences, HICSS-44 2010 - Koloa, Kauai, HI, United States
Duration: Jan 4 2011Jan 7 2011

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
ISSN (Print)1530-1605

Conference

Conference44th Hawaii International Conference on System Sciences, HICSS-44 2010
CountryUnited States
CityKoloa, Kauai, HI
Period1/4/111/7/11

ASJC Scopus subject areas

  • Engineering(all)

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    Dempsey, K., Bonasera, S., Bastola, D., & Ali, H. (2011). A novel correlation networks approach for the identification of gene targets. In Proceedings of the 44th Annual Hawaii International Conference on System Sciences, HICSS-44 2010 [5718537] (Proceedings of the Annual Hawaii International Conference on System Sciences). https://doi.org/10.1109/HICSS.2011.20