{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:46:57Z","timestamp":1765234017184,"version":"3.44.0"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T00:00:00Z","timestamp":1754352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Cell-type identification is one of the most important tasks in single-cell RNA Sequencing (scRNA-Seq) analysis. Recent research has revealed contrastive learning\u2019s great potential in handling multiple cell-type identification tasks.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this work, we proposed a novel augmentation-free scRNA-Seq contrastive learning (AF-RCL) algorithm, which simplifies the conventional data augmentation operation and adopts a new contrastive learning loss function. A large-scale empirical evaluation suggests that AF-RCL not only outperformed other contrastive learning-based cell-type identification methods but also obtained state-of-the-art predictive performance compared with other well-known cell-type identification methods. Further analysis also shows AF-RCL\u2019s advantages in learning high-quality discriminative feature representations based on scRNA-Seq expression profiles.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code is available at https:\/\/doi.org\/10.6084\/m9.figshare.28830311.v1 and at https:\/\/github.com\/ibrahimsaggaf\/AFRCL. The pre-trained AF-RCL encoders can be downloaded from https:\/\/doi.org\/10.5281\/zenodo.15109736, and the scRNA-Seq datasets used in this work can be downloaded from https:\/\/doi.org\/10.5281\/zenodo.8087611.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf437","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T17:14:43Z","timestamp":1755018883000},"source":"Crossref","is-referenced-by-count":2,"title":["Less is more: improving cell-type identification with augmentation-free single-cell RNA-Seq contrastive learning"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7379-5915","authenticated-orcid":false,"given":"Ibrahim","family":"Alsaggaf","sequence":"first","affiliation":[{"name":"School of Computing and Mathematical Sciences, Birkbeck, University of London , London WC1E 7HX,","place":["United 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