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dc.creatorZhang, W
dc.creatorFeng, D
dc.creatorLi, R
dc.creatorChernikov, A
dc.creatorChrisochoides, N
dc.creatorOsgood, C
dc.creatorKonikoff, C
dc.creatorNewfeld, S
dc.creatorKumar, S
dc.creatorJi, S
dc.date.accessioned2021-01-28T20:55:57Z
dc.date.available2021-01-28T20:55:57Z
dc.date.issued2013-12-28
dc.identifier.issn1471-2105
dc.identifier.issn1471-2105
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/5080
dc.identifier.other24373308 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/5098
dc.description.abstractBackground: Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.Results: We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.Conclusions: Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods. © 2013 Zhang et al.; licensee BioMed Central Ltd.
dc.format.extent372-372
dc.language.isoen
dc.relation.haspartBMC Bioinformatics
dc.relation.isreferencedbySpringer Science and Business Media LLC
dc.rightsCC BY
dc.subjectAnimals
dc.subjectArtificial Intelligence
dc.subjectCluster Analysis
dc.subjectComputational Biology
dc.subjectDrosophila
dc.subjectGene Expression Profiling
dc.subjectGene Expression Regulation, Developmental
dc.subjectImage Processing, Computer-Assisted
dc.subjectSoftware
dc.subjectSupport Vector Machine
dc.titleA mesh generation and machine learning framework for Drosophila gene expression pattern image analysis
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.1186/1471-2105-14-372
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.creator.orcidKumar, Sudhir|0000-0002-9918-8212
dc.date.updated2021-01-28T20:55:53Z
refterms.dateFOA2021-01-28T20:55:57Z


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