{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T23:05:52Z","timestamp":1783897552045,"version":"3.55.0"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T00:00:00Z","timestamp":1783814400000},"content-version":"vor","delay-in-days":11,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"National Institute of General Medical Sciences from National Institutes of Health","award":["P20 GM103434"],"award-info":[{"award-number":["P20 GM103434"]}]},{"name":"National Institute of General Medical Sciences from National Institutes of Health","award":["U54 GM104942"],"award-info":[{"award-number":["U54 GM104942"]}]},{"name":"National Institute of General Medical Sciences from National Institutes of Health","award":["2P20 GM121322"],"award-info":[{"award-number":["2P20 GM121322"]}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2125872"],"award-info":[{"award-number":["2125872"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2318708"],"award-info":[{"award-number":["2318708"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Accurate prediction of cell cycle phases is essential for mitigating confounding effects in single-cell RNA sequencing (scRNA-seq) analysis and for understanding how certain diseases (e.g. cancer) develop and respond to treatment. We evaluated traditional machine learning (AdaBoost, Random Forest, LightGBM) and deep learning approaches (DNN3, DNN5, CNN, hybrid models, ensembles) for cell cycle phase prediction from scRNA-seq data. Models were trained using consensus phase labels derived from four established tools (CellCycleScore, ccAFv2, Revelio, and Tricycle) applied to single-cell gene expression datasets representing diverse biological contexts, including a human leukemia cell line (REH), peripheral blood mononuclear cells (10\u00d7 PBMC), human pluripotent stem cells and their differentiated derivatives (GSE75748), 10\u00a0k embryonic mouse brain cells, and mouse hematopoietic stem and progenitor cells (GSE81682). Performance was evaluated on three independent datasets: human embryonic stem cells (GSE64016; 247 cells), human osteosarcoma cells (GSE146773; 1152 cells), and mouse embryonic stem cells (E-MTAB-2805; 288 cells), all with experimentally verified labels. REH-trained models achieved strong cross-dataset performance, reaching 74.35% accuracy on GSE146773 (Top 3 decision fusion (DF), and score fusion (SF)) and 72.9% on GSE64016, while attaining 56.9% on the cross-species Buettner mESC benchmark. SHapley Additive exPlanations (SHAP) analysis confirmed enrichment in cell-cycle-related gene sets for features. Notably, models trained on samples with lower recovery rates of cell cycle markers exhibited lower performance. These results highlight the strength of deep learning for robust, scalable cell cycle phase prediction from scRNA-seq data.<\/jats:p>","DOI":"10.1093\/bib\/bbag342","type":"journal-article","created":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T22:43:56Z","timestamp":1783896236000},"source":"Crossref","is-referenced-by-count":0,"title":["Deep learning models for cell cycle phase prediction from single-cell RNA sequencing data"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5687-3578","authenticated-orcid":false,"given":"Halima","family":"Akhter","sequence":"first","affiliation":[{"name":"Lane Department of Computer Science and Electrical Engineering, West Virginia University , 1220 Evansdale Drive, Morgantown, WV 26506 ,","place":["United States"]},{"name":"Department of Microbiology, Immunology & Cell Biology, School of Medicine, West Virginia University , 64 Medical Center Drive, Morgantown, WV 26506 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debra","family":"Piktel","sequence":"additional","affiliation":[{"name":"Department of Microbiology, Immunology & Cell Biology, School of Medicine, West Virginia University , 64 Medical Center Drive, Morgantown, WV 26506 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura F","family":"Gibson","sequence":"additional","affiliation":[{"name":"School of Medicine , West Virginia University, 64 Medical Center Drive, Morgantown, WV 26506 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gangqing","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Microbiology, Immunology & Cell Biology, School of Medicine, West Virginia University , 64 Medical Center Drive, Morgantown, WV 26506 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donald 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