{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T19:44:02Z","timestamp":1792266242984,"version":"4.4.0"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62422113"],"award-info":[{"award-number":["62422113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271329"],"award-info":[{"award-number":["62271329"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62322112"],"award-info":[{"award-number":["62322112"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62131004"],"award-info":[{"award-number":["62131004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Polytechnic University Research Fund","award":["6025310047K"],"award-info":[{"award-number":["6025310047K"]}],"id":[{"id":"https:\/\/ror.org\/00d2w9g53","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>Accurate prediction of single-cell responses to external stimuli is pivotal for deciphering gene regulatory mechanisms and accelerating data-driven drug discovery. However, effectively capturing the complex, non-linear mapping between intrinsic cell states and external stimuli remains an open problem.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose scLDM, a generative framework based on latent diffusion models for predicting single-cell perturbation responses. scLDM first compresses high-dimensional gene expression into a compact latent space via a variational autoencoder, followed by a conditional diffusion process to generate post-perturbation states, explicitly guided by pre-perturbation cellular state, cell type, and perturbation type. Systematic evaluations on six datasets across diverse biological settings, spanning pharmacological stimulation, viral infection, helminth infection, genetic perturbations, and multi-species immune response contexts, demonstrate that scLDM achieves superior predictive accuracy compared to state-of-the-art methods. Furthermore, the model exhibits strong interpretability, as the learned perturbation embeddings show high functional alignment with known biological mechanisms. Overall, scLDM provides a robust and biologically consistent strategy for in silico perturbation screening.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability<\/jats:title>\n                    <jats:p>The code is available at https:\/\/github.com\/samrogers1233\/scLDM and archived on Zenodo at https:\/\/doi.org\/10.5281\/zenodo.22658164.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag727","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T11:25:35Z","timestamp":1790594735000},"source":"Crossref","is-referenced-by-count":0,"title":["scLDM: a conditional diffusion framework for single-cell perturbation prediction"],"prefix":"10.1093","volume":"42","author":[{"given":"Boyang","family":"Wu","sequence":"first","affiliation":[{"name":"School of Information, Xiamen University , Xiamen, 361005,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhang","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Applied Sciences, Macao Polytechnic University , Macao SAR, 999078,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Electronic and Communication Engineering, Shenzhen Polytechnic University , Shenzhen, 518055,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9885-1978","authenticated-orcid":false,"given":"Xiangrong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information, Xiamen University , Xiamen, 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