A mesh generation and machine learning framework for Drosophila gene expression pattern image analysis
dc.creator | Zhang, W | |
dc.creator | Feng, D | |
dc.creator | Li, R | |
dc.creator | Chernikov, A | |
dc.creator | Chrisochoides, N | |
dc.creator | Osgood, C | |
dc.creator | Konikoff, C | |
dc.creator | Newfeld, S | |
dc.creator | Kumar, S | |
dc.creator | Ji, S | |
dc.date.accessioned | 2021-01-28T20:55:57Z | |
dc.date.available | 2021-01-28T20:55:57Z | |
dc.date.issued | 2013-12-28 | |
dc.identifier.issn | 1471-2105 | |
dc.identifier.issn | 1471-2105 | |
dc.identifier.doi | http://dx.doi.org/10.34944/dspace/5080 | |
dc.identifier.other | 24373308 (pubmed) | |
dc.identifier.uri | http://hdl.handle.net/20.500.12613/5098 | |
dc.description.abstract | 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. | |
dc.format.extent | 372-372 | |
dc.language.iso | en | |
dc.relation.haspart | BMC Bioinformatics | |
dc.relation.isreferencedby | Springer Science and Business Media LLC | |
dc.rights | CC BY | |
dc.subject | Animals | |
dc.subject | Artificial Intelligence | |
dc.subject | Cluster Analysis | |
dc.subject | Computational Biology | |
dc.subject | Drosophila | |
dc.subject | Gene Expression Profiling | |
dc.subject | Gene Expression Regulation, Developmental | |
dc.subject | Image Processing, Computer-Assisted | |
dc.subject | Software | |
dc.subject | Support Vector Machine | |
dc.title | A mesh generation and machine learning framework for Drosophila gene expression pattern image analysis | |
dc.type | Article | |
dc.type.genre | Journal Article | |
dc.relation.doi | 10.1186/1471-2105-14-372 | |
dc.ada.note | For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu | |
dc.creator.orcid | Kumar, Sudhir|0000-0002-9918-8212 | |
dc.date.updated | 2021-01-28T20:55:53Z | |
refterms.dateFOA | 2021-01-28T20:55:57Z |