Identifying spatially similar gene expression patterns in early stage fruit fly embryo images: binary feature versus invariant m

Background: Modern 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. Results: The 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...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Rajalakshmi Gurunathan, Bernard Van Emden, Sethuraman Panchanathan, Sudhir Kumar
• Keywords: BMCBI 2004, Expression Patterns, Gene Expression, Gene Expression Patterns