A mesh generation and machine learning framework for Drosophila gene expression pattern image analysis
Genre
Journal ArticleDate
2013-12-28Author
Zhang, WFeng, D
Li, R
Chernikov, A
Chrisochoides, N
Osgood, C
Konikoff, C
Newfeld, S
Kumar, S
Ji, S
Subject
AnimalsArtificial Intelligence
Cluster Analysis
Computational Biology
Drosophila
Gene Expression Profiling
Gene Expression Regulation, Developmental
Image Processing, Computer-Assisted
Software
Support Vector Machine
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http://hdl.handle.net/20.500.12613/5098
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Show full item recordDOI
10.1186/1471-2105-14-372Abstract
Background: 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.Citation to related work
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http://dx.doi.org/10.34944/dspace/5080