ArticleBMC bioinformatics2004
Identifying spatially similar gene expression patterns in early stage fruit fly embryo images: binary feature versus invariant moment digital representations.
Article in BMC bioinformatics, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- FlyExpress 7: An Integrated Discovery Platform To Study Coexpressed Genes UsingG3 (Bethesda, Md.) · 2017Article
- Learning sparse representations for fruit-fly gene expression pattern image annotation and retrieval.BMC bioinformatics · 2012Article
- Medium-throughput processing of whole mount in situ hybridisation experiments into gene expression domains.PloS one · 2012Article
- Comparison of embryonic expression within multigene families using the FlyExpress discovery platform reveals more spatial than temporal divergence.Developmental dynamics : an official publication of the American Association of Anatomists · 2012Article
- Systematic image-driven analysis of the spatial Drosophila embryonic expression landscape.Molecular systems biology · 2010Article
- Association of orthodenticle with natural variation for early embryonic patterning in Drosophila melanogaster.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2009Article
- Drosophila Gene Expression Pattern Annotation Using Sparse Features and Term-Term Interactions.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2009Article
- A bag-of-words approach for Drosophila gene expression pattern annotation.BMC bioinformatics · 2009Article
- Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning.IJCAI : proceedings of the conference · 2009Article
- A combinatorial code for pattern formation in Drosophila oogenesis.Developmental cell · 2008Article
- Automated annotation of Drosophila gene expression patterns using a controlled vocabulary.Bioinformatics (Oxford, England) · 2008Article
- Bioimage informatics: a new area of engineering biology.Bioinformatics (Oxford, England) · 2008Review
- Developmental Stage Annotation of Drosophila Gene Expression Pattern Images via an Entire Solution Path for LDA.ACM transactions on knowledge discovery from data · 2008Article
- Automatic image analysis for gene expression patterns of fly embryos.BMC cell biology · 2007Article
- Global analysis of patterns of gene expression during Drosophila embryogenesis.Genome biology · 2007Article
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Authors and funding
4 authors.
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Abstract
backgroundModern developmental biology relies heavily on the analysis of embryonic gene expression patterns. Investigators manually inspect hundreds or thousands of expression patterns to identify those that are spatially similar and to ultimately infer potential gene interactions. However, the rapid accumulation of gene expression pattern data over the last two decades, facilitated by high-throughput techniques, has produced a need for the development of efficient approaches for direct comparison of images, rather than their textual descriptions, to identify spatially similar expression patterns.
resultsThe effectiveness of the Binary Feature Vector (BFV) and Invariant Moment Vector (IMV) based digital representations of the gene expression patterns in finding biologically meaningful patterns was compared for a small (226 images) and a large (1819 images) dataset. For each dataset, an ordered list of images, with respect to a query image, was generated to identify overlapping and similar gene expression patterns, in a manner comparable to what a developmental biologist might do. The results showed that the BFV representation consistently outperforms the IMV representation in finding biologically meaningful matches when spatial overlap of the gene expression pattern and the genes involved are considered. Furthermore, we explored the value of conducting image-content based searches in a dataset where individual expression components (or domains) of multi-domain expression patterns were also included separately. We found that this technique improves performance of both IMV and BFV based searches.
conclusionsWe conclude that the BFV representation consistently produces a more extensive and better list of biologically useful patterns than the IMV representation. The high quality of results obtained scales well as the search database becomes larger, which encourages efforts to build automated image query and retrieval systems for spatial gene expression patterns.
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