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Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning for biological systems

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Published in:PLoS Computational Biology
Format: Online Article RSS Article
Published: 2026
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container_title PLoS Computational Biology
description
discipline_display Natural Sciences
discipline_facet Natural Sciences
format Online Article
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genre Journal Article
id rss_article:20922
institution FRELIP
journal_source_facet PLoS Computational Biology
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning for biological systems
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 Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning for biological systems
title_auth Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning for biological systems
title_full Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning for biological systems
title_fullStr Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning for biological systems
title_full_unstemmed Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning for biological systems
title_short Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning for biological systems
title_sort enhancing generalizability of model discovery across parameter space with multi-experiment equation learning for biological systems
topic Biology
Natural Sciences — Life Sciences
Natural Sciences
url https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014161