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Fluoro-forest: a random forest workflow for cell type annotation in high-dimensional immunofluorescence imaging with limited training data

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Bibliographic Details
Published in:Bioinformatics Advances : Journal of the International Society for Computational Biology
Format: Online Article RSS Article
Published: 2025
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container_title Bioinformatics Advances : Journal of the International Society for Computational Biology
description
discipline_display Natural Sciences
discipline_facet Natural Sciences
format Online Article
RSS Article
genre Journal Article
id rss_article:18825
institution FRELIP
journal_source_facet Bioinformatics Advances : Journal of the International Society for Computational Biology
publishDate 2025
publishDateSort 2025
record_format rss_article
spellingShingle Fluoro-forest: a random forest workflow for cell type annotation in high-dimensional immunofluorescence imaging with limited training data
Biology
Natural Sciences — Life Sciences
Natural Sciences
sub_discipline_display Natural Sciences — Life Sciences
sub_discipline_facet Natural Sciences — Life Sciences
subject_display Biology
Natural Sciences — Life Sciences
Natural Sciences
Biology
Natural Sciences — Life Sciences
Natural Sciences
subject_facet Biology
Natural Sciences — Life Sciences
Natural Sciences
title Fluoro-forest: a random forest workflow for cell type annotation in high-dimensional immunofluorescence imaging with limited training data
title_auth Fluoro-forest: a random forest workflow for cell type annotation in high-dimensional immunofluorescence imaging with limited training data
title_full Fluoro-forest: a random forest workflow for cell type annotation in high-dimensional immunofluorescence imaging with limited training data
title_fullStr Fluoro-forest: a random forest workflow for cell type annotation in high-dimensional immunofluorescence imaging with limited training data
title_full_unstemmed Fluoro-forest: a random forest workflow for cell type annotation in high-dimensional immunofluorescence imaging with limited training data
title_short Fluoro-forest: a random forest workflow for cell type annotation in high-dimensional immunofluorescence imaging with limited training data
title_sort fluoro-forest: a random forest workflow for cell type annotation in high-dimensional immunofluorescence imaging with limited training data
topic Biology
Natural Sciences — Life Sciences
Natural Sciences
url https://academic.oup.com/bioinformaticsadvances/article/doi/10.1093/bioadv/vbaf320/8404460?rss=1