Learning to capture the functions of genetic regulatory networks using graph motifs

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dc.contributor.authorKrishnamurthy, E.Vikram
dc.contributor.authorMurthy, Venu
dc.date.accessioned2015-12-10T23:18:17Z
dc.date.issued2012
dc.date.updated2016-02-24T08:38:14Z
dc.description.abstractNetwork motifs have been suggested as a means to understand Systems biological functions. Based on several arguments from computer science, logic, mathematics, chemistry and non-linear dynamics, we explain the difficulties encountered in capturing the evolving dynamical functions carried out by the genetic regulatory networks (GRN), based on the assembly of motifs.
dc.identifier.issn1755-0386
dc.identifier.urihttp://hdl.handle.net/1885/65554
dc.publisherInderscience Publishers
dc.sourceInternational Journal of Advanced Intelligence Paradigms
dc.subjectKeywords: Adjacency matrices; Assortative networks; CN; Complex network; Disassortative networks; Eigenvalues; Graphs; Lyapunov exponents; Motifs; Positive entropy; System biology
dc.titleLearning to capture the functions of genetic regulatory networks using graph motifs
dc.typeJournal article
local.bibliographicCitation.issue2
local.bibliographicCitation.lastpage197
local.bibliographicCitation.startpage185
local.contributor.affiliationKrishnamurthy, E.Vikram, College of Engineering and Computer Science, ANU
local.contributor.affiliationMurthy, Venu, College of Engineering and Computer Science, ANU
local.contributor.authoruidKrishnamurthy, E.Vikram, v002692
local.contributor.authoruidMurthy, Venu, v008096
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor080100 - ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
local.identifier.ariespublicationf5625xPUB1123
local.identifier.citationvolume4
local.identifier.doi10.1504/IJAIP.2012.048146
local.identifier.scopusID2-s2.0-84864569238
local.type.statusPublished Version

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