A mesh generation and machine learning framework for Drosophila gene expression pattern image analysis.
BMC Bioinformatics
; 14: 372, 2013 Dec 28.
Article
em En
| MEDLINE
| ID: mdl-24373308
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
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Inteligência Artificial
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Regulação da Expressão Gênica no Desenvolvimento
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Biologia Computacional
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Máquina de Vetores de Suporte
Tipo de estudo:
Prognostic_studies
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Qualitative_research
Limite:
Animals
Idioma:
En
Ano de publicação:
2013
Tipo de documento:
Article