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dc.contributor.authorChaiboonchoe, Amphunen
dc.contributor.authorSamarasinghe, Sandhyaen
dc.contributor.authorKulasiri, Gamalathge D.en
dc.date.accessioned2011-06-02T00:04:55Z
dc.date.issued2009-07en
dc.identifier.isbn978-0-9758400-7-8en
dc.identifier.urihttps://hdl.handle.net/10182/3590
dc.description.abstractAcute lymphoblastic leukemia (ALL) causes the highest number of deaths from cancer in children aged between one and fourteen. The most common treatment for children with ALL is chemotherapy, a cancer treatment that uses drugs to kill cancer cells or stop cell division. The drug and dosage combinations may vary for each child. Unfortunately, chemotherapy treatments may cause serious side effects. Glucocorticoids (GCs) have been used as therapeutic agents for children with ALL for more than 50 years. Common and widely drugs in this class include prednisolone and dexamethasone. Childhood leukemia now has a survival rate of 80% (Pui, Robison, & Look, 2008). The key clinical question is identifying those children who will not respond well to established therapy strategies.GCs regulate diverse biological processes, for example, metabolism, development, differentiation, cell survival and immunity. GCs induce apoptosis and G1 cell cycle arrest in lymphoid cells. In fact, not much is known about the molecular mechanism of GCs sensitivity and resistance, and GCs-induced apoptotic signal transduction pathways and there are many controversial hypotheses about both genes regulated by GCs and potential molecular mechanism of GCs-induced apoptosis. Therefore, understanding the mechanism of this drug should lead to better prognostic factors (treatment response), more targeted therapies and prevention of side effects. GCs induced apoptosis have been studied by using microarray technology in vivo and in vitro on samples consisting of GCs treated ALL cell lines, mouse thymocytes and/or ALL patients. However, time series GCs treated childhood ALL datasets are currently extremely limited. DNA microarrays are essential tools for analysis of expression of many genes simultaneously. Gene expression data show the level of activity of several genes under experimental conditions. Genes with similar expression patterns could belong to the same pathway or have similar function. DNA microarray data analysis has been carried out using statistical analysis as well as machine learning and data mining approaches. There are many microarray analysis tools; this study aims to combine emergent clustering methods to get meaningful biological insights into mechanisms underlying GCs induced apoptosis. In this study, microarray data originated from prednisolone (glucocorticoids) treated childhood ALL samples (Schmidt et al., 2006) (B-linage and T-linage) and collected at 6 and 24 hours after treatment are analysed using four methods: Selforganizing maps (SOMs), Emergent self-organizing maps (ESOM) (Ultsch & Morchen, 2005), the Short Time series Expression Miner (STEM) (Ernst & Bar-Joseph, 2006) and Fuzzy clustering by Local Approximation of MEmbership (FLAME) (Fu & Medico, 2007). The results revealed intrinsic biological patterns underlying the GCs time series data: there are at least five different gene activities happening during the three time points; GCs-induced apoptotic genes were identified; and genes active at both time points or only at 6 hours or 24 hours were determined. Also, interesting gene clusters with membership in already known pathways were found thereby providing promising candidate gens for further inferring GCs induced apoptotic gene regulatory networks.en
dc.format.extent741-747en
dc.language.isoenen
dc.publisherModelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulationen
dc.relationThe original publication is available from - Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation - http://mssanz.org.au/modsim09en
dc.rightsCopyright © The Authors. The responsibility for the contents of this paper rests upon the authors and not on the Modelling and Simulation Society of Australia and New Zealand Inc.en
dc.sourceJoint 18th World IMACS/MODSIM09en
dc.subjectemergent self organising mapsen
dc.subjectchildhood leukaemiaen
dc.subjectshort time series gene expressionen
dc.subjectclusteringen
dc.subjectglucocorticoidsen
dc.subjectChildhood Leukemiaen
dc.subjectEmergent self-organizing mapsen
dc.titleUsing emergent clustering methods to analyse short time series gene expression data from childhood leukemia treated with glucocorticoidsen
dc.typeConference Contribution - Published
lu.contributor.unitLincoln Universityen
lu.contributor.unitFaculty of Agriculture and Life Sciencesen
lu.contributor.unitDepartment of Wine, Food and Molecular Biosciencesen
lu.contributor.unitFaculty of Environment, Society and Designen
lu.contributor.unitDepartment of Environmental Managementen
pubs.finish-date2009-07-17en
pubs.notesPaper presented at the 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation, Cairns, Australia, 13–17 July 2009.en
pubs.organisational-group/LU
pubs.organisational-group/LU/Agriculture and Life Sciences
pubs.organisational-group/LU/Agriculture and Life Sciences/WFMB
pubs.organisational-group/LU/Faculty of Environment, Society and Design
pubs.organisational-group/LU/Faculty of Environment, Society and Design/DEM
pubs.organisational-group/LU/Research Management Office
pubs.organisational-group/LU/Research Management Office/QE18
pubs.publication-statusPublisheden
pubs.publisher-urlhttp://mssanz.org.au/modsim09en
pubs.start-date2009-07-13en
lu.identifier.orcid0000-0001-8744-1578
lu.identifier.orcid0000-0003-2943-4331
lu.subtypeConference Paperen


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