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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Transformers in single-cell omics: A review and new perspectives.
Nat. Methods 21, 1430-1443 (2024)
Recent efforts to construct reference maps of cellular phenotypes have expanded the volume and diversity of single-cell omics data, providing an unprecedented resource for studying cell properties. Despite the availability of rich datasets and their continued growth, current single-cell models are unable to fully capitalize on the information they contain. Transformers have become the architecture of choice for foundation models in other domains owing to their ability to generalize to heterogeneous, large-scale datasets. Thus, the question arises of whether transformers could set off a similar shift in the field of single-cell modeling. Here we first describe the transformer architecture and its single-cell adaptations and then present a comprehensive review of the existing applications of transformers in single-cell analysis and critically discuss their future potential for single-cell biology. By studying limitations and technical challenges, we aim to provide a structured outlook for future research directions at the intersection of machine learning and single-cell biology.
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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Review
ISSN (print) / ISBN
1548-7091
e-ISSN
1548-7105
Zeitschrift
Nature Methods
Quellenangaben
Band: 21,
Heft: 8,
Seiten: 1430-1443
Verlag
Nature Publishing Group
Verlagsort
New York, NY
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Computational Biology (ICB)