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The impact of biologically relevant negative samples on machine learning-based B-cell epitope prediction for Influenza A

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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 Biology
discipline_facet Biology
format Online Article
RSS Article
genre Journal Article
id rss_article:81550
institution FRELIP
journal_source_facet Bioinformatics Advances : Journal of the International Society for Computational Biology
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle The impact of biologically relevant negative samples on machine learning-based B-cell epitope prediction for Influenza A
Biology
General
Biology
sub_discipline_display General
sub_discipline_facet General
subject_display Biology
General
Biology
Biology
General
Biology
subject_facet Biology
General
Biology
title The impact of biologically relevant negative samples on machine learning-based B-cell epitope prediction for Influenza A
title_auth The impact of biologically relevant negative samples on machine learning-based B-cell epitope prediction for Influenza A
title_full The impact of biologically relevant negative samples on machine learning-based B-cell epitope prediction for Influenza A
title_fullStr The impact of biologically relevant negative samples on machine learning-based B-cell epitope prediction for Influenza A
title_full_unstemmed The impact of biologically relevant negative samples on machine learning-based B-cell epitope prediction for Influenza A
title_short The impact of biologically relevant negative samples on machine learning-based B-cell epitope prediction for Influenza A
title_sort the impact of biologically relevant negative samples on machine learning-based b-cell epitope prediction for influenza a
topic Biology
General
Biology
url https://academic.oup.com/bioinformaticsadvances/article/doi/10.1093/bioadv/vbag127/8667275?rss=1