{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T12:59:04Z","timestamp":1791464344965,"version":"4.3.4"},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"vor","delay-in-days":37,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62166014"],"award-info":[{"award-number":["62166014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012547","name":"Natural Science Foundation of Guangxi Zhuang Autonomous Region","doi-asserted-by":"publisher","award":["2025GXNSFAA069627"],"award-info":[{"award-number":["2025GXNSFAA069627"]}],"id":[{"id":"10.13039\/100012547","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Single-cell multi-omics technologies provide complementary views of cellular states, but are challenged by pervasive feature sparsity, pronounced cross-modality heterogeneity, and a critical shortage of labeled cells, hindering accurate cell-type annotation. We propose dual-masked contrastive learning (DMCL), a topology-guided weakly supervised cross-modal representation learning framework that addresses these challenges within a unified architecture. DMCL first constructs modality-specific K-nearest neighbor graphs to capture cell\u2013cell topological relationships within each omics layer. A dual masking strategy combining global column-wise masking and local random masking is then applied to simulate realistic feature missingness and regularize the model against technical noise. Topology-aware latent representations are subsequently derived through a graph autoencoder that faithfully preserves the local cellular neighborhood structure of each modality. Cross-modal integration is achieved via a multi-head self-attention module that dynamically weights modality-specific features according to per-cell informativeness, enabling context-adaptive modality fusion. To further improve representation quality, DMCL incorporates a dual contrastive objective that jointly enforces intra-cell view consistency and inter-cell neighborhood structure, thereby enhancing both cross-modality alignment and structural preservation in the latent space. Extensive experiments on diverse real and simulated datasets demonstrate that DMCL achieves accurate cell-type annotation and superior clustering performance under minimal label supervision, establishing a principled and scalable foundation for weakly supervised single-cell multimodal analysis.<\/jats:p>","DOI":"10.1093\/bib\/bbag546","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T19:24:30Z","timestamp":1789673070000},"source":"Crossref","is-referenced-by-count":0,"title":["Topology-guided dual-masked contrastive learning for weakly supervised cell-type annotation in single-cell multi-omics"],"prefix":"10.1093","volume":"27","author":[{"given":"Yi","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Guilin University of Technology , 12 Jiangan Road, Qixing District, Guilin 541004 ,","place":["China"]},{"name":"Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin 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