Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression data

Background: Retinal photoreceptors are highly specialised cells, which detect light and are central to mammalian vision. Many retinal diseases occur as a result of inherited dysfunction of the rod and cone photoreceptor cells. Development and maintenance of photoreceptors requires appropriate regulation of the many genes specifically or highly expressed in these cells. Over the last decades, different experimental approaches have been developed to identify photoreceptor enriched genes. Recent progress in RNA analysis technology has generated large amounts of gene expression data relevant to retinal development. This paper assesses a machine learning methodology for supporting the identification of photoreceptor enriched genes based on expression data. Results: Based on the analysis of publicly-available gene expression data from the developing mouse retina generated by serial analysis of gene expression (SAGE), this paper presents a predictive methodology comprising several in silico ...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Haiying Wang, Huiru Zheng, David Simpson, Francisco Azuaje
• Keywords: BMCBI 2006, Gene Expression, Gene Expression Data, Photoreceptor Cells