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ISBI
2009
IEEE

Automated Analysis of Human Protein Atlas Immunofluorescence Images

8 years 11 months ago
Automated Analysis of Human Protein Atlas Immunofluorescence Images
The Human Protein Atlas is a rich source of location proteomics data. In this work, we present an automated approach for processing and classifying major subcellular patterns in the Atlas images. We demonstrate that two different classification frameworks (support vector machine and random forest) are effective at determining subcellular locations; we can analyze over 3500 Atlas images with a high degree of accuracy, up to 87.5% for all of the samples and 98.5% when only considering samples in whose classification assignments we are most confident. Moreover, the features obtained in both of these frameworks are observed to be highly consistent and generalizable. Additionally, we observe that the features relating the proteins to cell markers are especially important in automated learning approaches.
Justin Newberg, Jieyue Li, Arvind Rao, Fredrik Pon
Added 19 May 2010
Updated 19 May 2010
Type Conference
Year 2009
Where ISBI
Authors Justin Newberg, Jieyue Li, Arvind Rao, Fredrik Ponten, Mathias Uhlen, Emma Lundberg, Robert F. Murphy
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