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rdf:type |
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lifeskim:mentions |
umls-concept:C0017262,
umls-concept:C0017337,
umls-concept:C0020792,
umls-concept:C0185117,
umls-concept:C0376284,
umls-concept:C1292724,
umls-concept:C1511726,
umls-concept:C1521721,
umls-concept:C1527178,
umls-concept:C1705938,
umls-concept:C2911684
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pubmed:dateCreated |
2006-4-3
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pubmed:abstractText |
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.
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pubmed:commentsCorrections |
http://linkedlifedata.com/resource/pubmed/commentcorrection/16524483-10699188,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16524483-10802659,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16524483-11301299,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16524483-11733058,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16524483-12364383,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16524483-12391299,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16524483-12537556,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16524483-12613259,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16524483-12874019,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16524483-15163632,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16524483-15226823,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16524483-15239836,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16524483-2902634,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16524483-7570003,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16524483-9834183
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pubmed:language |
eng
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pubmed:journal |
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pubmed:citationSubset |
IM
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pubmed:chemical |
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pubmed:status |
MEDLINE
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pubmed:issn |
1471-2105
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pubmed:author |
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pubmed:issnType |
Electronic
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pubmed:volume |
7
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
116
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pubmed:dateRevised |
2009-11-18
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pubmed:meshHeading |
pubmed-meshheading:16524483-Animals,
pubmed-meshheading:16524483-Artificial Intelligence,
pubmed-meshheading:16524483-Computer Simulation,
pubmed-meshheading:16524483-Eye Proteins,
pubmed-meshheading:16524483-Feasibility Studies,
pubmed-meshheading:16524483-Gene Expression Profiling,
pubmed-meshheading:16524483-Humans,
pubmed-meshheading:16524483-Models, Biological,
pubmed-meshheading:16524483-Pattern Recognition, Automated,
pubmed-meshheading:16524483-Photoreceptor Cells,
pubmed-meshheading:16524483-Signal Transduction,
pubmed-meshheading:16524483-Transcription Factors
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pubmed:year |
2006
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pubmed:articleTitle |
Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression data.
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pubmed:affiliation |
School of Computing and Mathematics, University of Ulster, UK. wang@ulster.ac.uk
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pubmed:publicationType |
Journal Article,
Evaluation Studies
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