title: A mesh generation and machine learning framework for Drosophila gene expression pattern image analysis.
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.
authors.forename: Wenlu
authors.initials: W
authors.lastname: Zhang
authors.forename: Daming
authors.initials: D
authors.lastname: Feng
authors.forename: Rongjian
authors.initials: R
authors.lastname: Li
authors.forename: Andrey
authors.initials: A
authors.lastname: Chernikov
authors.forename: Nikos
authors.initials: N
authors.lastname: Chrisochoides
authors.forename: Christopher
authors.initials: C
authors.lastname: Osgood
authors.forename: Charlotte
authors.initials: C
authors.lastname: Konikoff
authors.forename: Stuart
authors.initials: S
authors.lastname: Newfeld
authors.forename: Sudhir
authors.initials: S
authors.lastname: Kumar
authors.affiliation: Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA. sji@cs.odu.edu.
authors.forename: Shuiwang
authors.initials: S
authors.lastname: Ji
mesh_terms: D000818:Animals; D001185:Artificial Intelligence / Q000592:standards; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods* / Q000592:standards; D004330:Drosophila / Q000196:embryology / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods; D018507:Gene Expression Regulation, Developmental; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000592:standards* / Q000706:statistics & numerical data; D012984:Software; D060388:Support Vector Machine