{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T21:25:01Z","timestamp":1787261101758,"version":"build-2736575974"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"content-version":"vor","delay-in-days":210,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009122","name":"Ministry of Education","doi-asserted-by":"publisher","award":["NRF-2022R1A2C1092497"],"award-info":[{"award-number":["NRF-2022R1A2C1092497"]}],"id":[{"id":"10.13039\/100009122","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Understanding disease dynamics from longitudinal multi-omics is hindered by traditional approaches that focus on univariate trajectories and static networks while ignoring temporal evolution. We developed DiDyNet, a framework for identifying phenotype-specific temporal molecular networks by defining dynamic coupling as coordinated molecular trajectories. DiDyNet operates through four steps: (i) two-dimensional variance-based filtering to prioritize dynamic features; (ii) quantification of subject-specific coordination using Dynamic Time Warping to accommodate asynchrony; (iii) statistical testing for differential dynamic couplings; and (iv) linear mixed model-based post-hoc refinement to distinguish genuine coordinated dynamics from stochastic noise.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Simulation studies showed that DiDyNet significantly outperformed static summary statistics, including the mean, median, and difference, which cannot capture dynamic signals. Dynamic Time Warping-based quantification also demonstrated greater robustness than Euclidean distance, correlation-based distance, and constrained alignment methods under temporal misalignment and signal sparsity. Application to an insulin resistance cohort identified a coordinated cross-omics network linking systemic inflammation with intracellular stress responses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability<\/jats:title>\n                    <jats:p>Source code is freely available at https:\/\/github.com\/bioinfoliu\/DiDyNet.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioadv\/vbag192","type":"journal-article","created":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:55:29Z","timestamp":1785412529000},"source":"Crossref","is-referenced-by-count":0,"title":["DiDyNet: a robust framework for differential dynamic network inference from longitudinal multi-omics data"],"prefix":"10.1093","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2819-0496","authenticated-orcid":false,"given":"Zhe","family":"Liu","sequence":"first","affiliation":[{"name":"Interdisciplinary Program in Bioinformatics, 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