{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:03:15Z","timestamp":1792785795874,"version":"4.5.0"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"vor","delay-in-days":12,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhongguancun Academy","award":["C20250607"],"award-info":[{"award-number":["C20250607"]}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["4264118"],"award-info":[{"award-number":["4264118"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Beijing University of Posts and Telecommunications","award":["2025AI4S19"],"award-info":[{"award-number":["2025AI4S19"]}],"id":[{"id":"https:\/\/ror.org\/04w9fbh59","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Predicting transcriptional responses to drug perturbations in unseen cellular contexts is a central challenge in single-cell perturbation modeling. This challenge creates a need for models that combine flexible state transitions with structured biological prior knowledge. Existing data-driven models often struggle to generalize across unseen cell types, while many prior-based models rely on static biological graphs without explicitly conditioning prior influence on perturbation-specific target support.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose DGP-LA, a dynamic graph-prior latent adaptation framework that refines biological priors and injects them as gated latent corrections within a cell-state-conditioned transition model. DGP-LA couples a transition backbone with perturbation-conditioned sparse graph refinement. It propagates gene-aligned target support over the refined graph and selectively injects the resulting topology-aware signal into the hidden representation. On Tahoe and NeurIPS, DGP-LA improves Pearson Delta relative to State by 8.7% and 35.8% for generalization across unseen cell types. In zero-shot drug perturbation response prediction on unseen cell types, it improves Pearson Delta relative to CRISP by 79.1% and 67.6%. The model also improves DE-gene recovery, response discrimination, and Hallmark pathway recovery, supporting adaptive latent integration of biological priors for more generalizable and biologically interpretable perturbation modeling.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability<\/jats:title>\n                    <jats:p>DGP-LA\u2019s source code and reproducibility materials are available at https:\/\/github.com\/bieyl\/DGP-LA and archived on Zenodo at https:\/\/doi.org\/10.5281\/zenodo.22155398.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag747","type":"journal-article","created":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T11:23:21Z","timestamp":1791804201000},"source":"Crossref","is-referenced-by-count":0,"title":["Dynamic graph prior-guided latent adaptation for generalizable single-cell perturbation response prediction"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3067-1266","authenticated-orcid":false,"given":"Yunlong","family":"Bie","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications , Beijing 100876,","place":["China"]},{"name":"Zhongguancun Academy , Beijing 100094,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peishan","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications , Beijing 100876,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5284-2259","authenticated-orcid":false,"given":"Qijin","family":"Yin","sequence":"additional","affiliation":[{"name":"Zhongguancun Academy , Beijing 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