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Here, we frame a more challenging and useful\n problem where previously unseen proteins are to be classified. Results:\n Using CD-tagging, we generated two new image datasets for evaluation of\n this problem, which contain several different proteins for each location\n class. Evaluation of previous methods on these new datasets showed that it\n is much harder to train a classifier that generalizes across different\n proteins than one that simply recognizes a protein it was trained on. We\n therefore developed and evaluated additional approaches, incorporating\n novel modifications of local features techniques. These extended the\n notion of local features to exploit both the protein image and any\n reference markers that were imaged in parallel. With these, we obtained a\n large accuracy improvement in our new datasets over existing methods.\n Additionally, these features help achieve classification improvements for\n other previously studied datasets.","descriptionType":"Abstract"},{"description":"Labeled RandTag\n Widefield Images (Unlabeled Subset)This datasets consists\n of fluorescent microscope images of GFP-tagged proteins locating to\n different organelles. The organelles were annotated by visual inspection.\n For each organelle, multiple proteins locating to it were identified; and\n for each protein, multiple images were acquired. Images contain two\n channels: GFP tagged protein and a nuclear marker (Hoechst). NIH 3T3 cells\n were tagged using CD tagging. This data was acquired on a widefield\n microscopeunlabeled.tar.bz2Labeled RandTag Confocal ImagesThis datasets consists of fluorescent microscope images of GFP-tagged proteins locating to different organelles. The organelles were annotated by visual inspection. For each organelle, multiple proteins locating to it were identified; and for each protein, multiple images were acquired. Images contain two channels: GFP tagged protein and a nuclear marker (Hoechst). NIH 3T3 cells were tagged using CD tagging. This data was acquired on a confocal microscope.labeled-confocal.tar.bz2Labeled RandTag Widefield Images (Nuclear Subset)Part of widefield labeled datasetnuclear.tar.bz2Labeled RandTag Widefield Images (Cytoskeleton Subset)This is part of the labeled widefield dataset.cytoskeleton.tar.bz2Labeled RandTag Widefield Images (Nucleoli Subset)This is part of the labeled widefield dataset.nucleoli.tar.bz2Labeled RandTag Widefield Images (Golgi Subset)This file is part of the labeled widefield dataset.Golgi.tar.bz2Labeled RandTag Widefield Images (Mitochondria Subset)Part of widefield labeled subsetmitochondria.tar.bz2Labeled RandTag Widefield Images (Cytoplasmic Subset)This is part of the labeled widefield dataset.cytoplasmic.tar.bz2Labeled RandTag Widefield Images (Lysosomal Subset)This is part of the labeled widefield datasetlysosome.tar.bz2Labeled RandTag Widefield Images (Plasma Membrane Subset)This file is part of the widefield labeled datasetmembrane.tar.bz2Labeled RandTag Widefield Images (ER Subset)This is part of the labeled widefield dataset.ER.tar.bz2","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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  <identifier identifierType="DOI">10.5061/DRYAD.2VM70</identifier>
  <creators>
    <creator>
      <creatorName nameType="Personal">Coelho, Luis Pedro</creatorName>
    </creator>
    <creator>
      <creatorName nameType="Personal">Kangas, Joshua D.</creatorName>
    </creator>
    <creator>
      <creatorName nameType="Personal">Naik, Armaghan W.</creatorName>
    </creator>
    <creator>
      <creatorName nameType="Personal">Osuna-Highley, Elvira</creatorName>
    </creator>
    <creator>
      <creatorName nameType="Personal">Glory-Afshar, Estelle</creatorName>
    </creator>
    <creator>
      <creatorName nameType="Personal">Fuhrman, Margaret</creatorName>
    </creator>
    <creator>
      <creatorName nameType="Personal">Simha, Ramanuja</creatorName>
    </creator>
    <creator>
      <creatorName nameType="Personal">Berget, Peter B.</creatorName>
    </creator>
    <creator>
      <creatorName nameType="Personal">Jarvik, Jonathan W.</creatorName>
    </creator>
    <creator>
      <creatorName nameType="Personal">Murphy, Robert F.</creatorName>
    </creator>
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  <titles>
    <title>
      Data from: Determining the subcellular location of new proteins from
      microscope images using local features
    </title>
  </titles>
  <publisher publisherIdentifier="https://ror.org/00x6h5n95" publisherIdentifierScheme="ROR" schemeURI="https://ror.org/">Dryad</publisher>
  <resourceType resourceTypeGeneral="Dataset">dataset</resourceType>
  <publicationYear>2013</publicationYear>
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    <subject subjectScheme="PLOS Subject Area Thesaurus" schemeURI="https://github.com/PLOS/plos-thesaurus">Bioinformatics</subject>
    <subject>fluorescent microscopy</subject>
    <subject>Mus musculus</subject>
    <subject>bioimage informatics</subject>
    <subject>subcellular location</subject>
    <subject>green fluorescent protein (GFP)</subject>
  </subjects>
  <dates>
    <date dateType="Submitted">2013-08-28T15:51:09Z</date>
    <date dateType="Issued">2013-08-30T16:57:48Z</date>
    <date dateType="Available">2013-08-30T16:57:48Z</date>
  </dates>
  <language>en</language>
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    <description descriptionType="Abstract">
      Motivation: Evaluation of previous systems for automated determination of
      subcellular location from microscope images has been done using datasets
      in which each location class consisted of multiple images of the same
      representative protein. Here, we frame a more challenging and useful
      problem where previously unseen proteins are to be classified. Results:
      Using CD-tagging, we generated two new image datasets for evaluation of
      this problem, which contain several different proteins for each location
      class. Evaluation of previous methods on these new datasets showed that it
      is much harder to train a classifier that generalizes across different
      proteins than one that simply recognizes a protein it was trained on. We
      therefore developed and evaluated additional approaches, incorporating
      novel modifications of local features techniques. These extended the
      notion of local features to exploit both the protein image and any
      reference markers that were imaged in parallel. With these, we obtained a
      large accuracy improvement in our new datasets over existing methods.
      Additionally, these features help achieve classification improvements for
      other previously studied datasets.
    </description>
    <description descriptionType="Other">
      &lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4
      class="o-heading__level3-file-title"&gt;Labeled RandTag
      Widefield Images (Unlabeled Subset)&lt;/h4&gt;&lt;div
      class="o-metadata__file-description"&gt;This datasets consists
      of fluorescent microscope images of GFP-tagged proteins locating to
      different organelles. The organelles were annotated by visual inspection.
      For each organelle, multiple proteins locating to it were identified; and
      for each protein, multiple images were acquired. Images contain two
      channels: GFP tagged protein and a nuclear marker (Hoechst). NIH 3T3 cells
      were tagged using CD tagging. This data was acquired on a widefield
      microscope&lt;/div&gt;&lt;div
      class="o-metadata__file-name"&gt;unlabeled.tar.bz2&lt;/br&gt;&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;Labeled RandTag Confocal Images&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;This datasets consists of fluorescent microscope images of GFP-tagged proteins locating to different organelles. The organelles were annotated by visual inspection. For each organelle, multiple proteins locating to it were identified; and for each protein, multiple images were acquired. Images contain two channels: GFP tagged protein and a nuclear marker (Hoechst). NIH 3T3 cells were tagged using CD tagging. This data was acquired on a confocal microscope.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;labeled-confocal.tar.bz2&lt;/br&gt;&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;Labeled RandTag Widefield Images (Nuclear Subset)&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;Part of widefield labeled dataset&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;nuclear.tar.bz2&lt;/br&gt;&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;Labeled RandTag Widefield Images (Cytoskeleton Subset)&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;This is part of the labeled widefield dataset.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;cytoskeleton.tar.bz2&lt;/br&gt;&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;Labeled RandTag Widefield Images (Nucleoli Subset)&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;This is part of the labeled widefield dataset.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;nucleoli.tar.bz2&lt;/br&gt;&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;Labeled RandTag Widefield Images (Golgi Subset)&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;This file is part of the labeled widefield dataset.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;Golgi.tar.bz2&lt;/br&gt;&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;Labeled RandTag Widefield Images (Mitochondria Subset)&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;Part of widefield labeled subset&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;mitochondria.tar.bz2&lt;/br&gt;&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;Labeled RandTag Widefield Images (Cytoplasmic Subset)&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;This is part of the labeled widefield dataset.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;cytoplasmic.tar.bz2&lt;/br&gt;&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;Labeled RandTag Widefield Images (Lysosomal Subset)&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;This is part of the labeled widefield dataset&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;lysosome.tar.bz2&lt;/br&gt;&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;Labeled RandTag Widefield Images (Plasma Membrane Subset)&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;This file is part of the widefield labeled dataset&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;membrane.tar.bz2&lt;/br&gt;&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;Labeled RandTag Widefield Images (ER Subset)&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;This is part of the labeled widefield dataset.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;ER.tar.bz2&lt;/br&gt;&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;
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