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Interpreting higher-order dependence in multimorbidity using cohort data: A partial information decomposition approach

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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 Biology
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genre Journal Article
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institution FRELIP
journal_source_facet PLoS Computational Biology
last_indexed 2026-06-11T02:00:41.891Z
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Interpreting higher-order dependence in multimorbidity using cohort data: A partial information decomposition approach
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 Interpreting higher-order dependence in multimorbidity using cohort data: A partial information decomposition approach
title_auth Interpreting higher-order dependence in multimorbidity using cohort data: A partial information decomposition approach
title_full Interpreting higher-order dependence in multimorbidity using cohort data: A partial information decomposition approach
title_fullStr Interpreting higher-order dependence in multimorbidity using cohort data: A partial information decomposition approach
title_full_unstemmed Interpreting higher-order dependence in multimorbidity using cohort data: A partial information decomposition approach
title_short Interpreting higher-order dependence in multimorbidity using cohort data: A partial information decomposition approach
title_sort interpreting higher-order dependence in multimorbidity using cohort data: a partial information decomposition approach
topic Biology
General
Biology
url https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014386