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scGeno: a Hidden Markov Model approach to denoise chromosome-scale genotypes from single-cell 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: 2026
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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:18741
institution FRELIP
journal_source_facet Bioinformatics Advances : Journal of the International Society for Computational Biology
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle scGeno: a Hidden Markov Model approach to denoise chromosome-scale genotypes from single-cell 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 scGeno: a Hidden Markov Model approach to denoise chromosome-scale genotypes from single-cell data
title_auth scGeno: a Hidden Markov Model approach to denoise chromosome-scale genotypes from single-cell data
title_full scGeno: a Hidden Markov Model approach to denoise chromosome-scale genotypes from single-cell data
title_fullStr scGeno: a Hidden Markov Model approach to denoise chromosome-scale genotypes from single-cell data
title_full_unstemmed scGeno: a Hidden Markov Model approach to denoise chromosome-scale genotypes from single-cell data
title_short scGeno: a Hidden Markov Model approach to denoise chromosome-scale genotypes from single-cell data
title_sort scgeno: a hidden markov model approach to denoise chromosome-scale genotypes from single-cell data
topic Biology
Natural Sciences — Life Sciences
Natural Sciences
url https://academic.oup.com/bioinformaticsadvances/article/doi/10.1093/bioadv/vbag094/8586734?rss=1