Automated analysis of human protein atlas immunofluorescence images
2009 (English)In: Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, IEEE , 2009, 1023-1026 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
IEEE , 2009. 1023-1026 p.
Feature selection, Image classification, Location proteomics, Machine learning, Microscopy
Radiology, Nuclear Medicine and Medical Imaging
IdentifiersURN: urn:nbn:se:kth:diva-153544DOI: 10.1109/ISBI.2009.5193229ISI: 000270678400261ScopusID: 2-s2.0-70449361696ISBN: 978-142443932-4OAI: oai:DiVA.org:kth-153544DiVA: diva2:753799
2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, 28 June 2009 through 1 July 2009, Boston, MA, United States
QC 201410092014-10-092014-10-062014-10-09Bibliographically approved