Hanhart, Daniel; Gossi, Federico; Rapsomaniki, Maria Anna; Kruithof-de Julio, Marianna; Chouvardas, Panagiotis (2024). ScLinear predicts protein abundance at single-cell resolution. Communications biology, 7(267) Springer Nature 10.1038/s42003-024-05958-4
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Single-cell multi-omics have transformed biomedical research and present exciting machine learning opportunities. We present scLinear, a linear regression-based approach that predicts single-cell protein abundance based on RNA expression. ScLinear is vastly more efficient than state-of-the-art methodologies, without compromising its accuracy. ScLinear is interpretable and accurately generalizes in unseen single-cell and spatial transcriptomics data. Importantly, we offer a critical view in using complex algorithms ignoring simpler, faster, and more efficient approaches.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Department of Dermatology, Urology, Rheumatology, Nephrology, Osteoporosis (DURN) > Clinic of Urology 04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DBMR Forschung Mu35 > Forschungsgruppe Urologie 04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DBMR Forschung Mu35 > Forschungsgruppe Urologie 04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) |
UniBE Contributor: |
Hanhart, Daniel Walter, Gossi, Federico, Kruithof-de Julio, Marianna, Chouvardas, Panagiotis |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
2399-3642 |
Publisher: |
Springer Nature |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
05 Mar 2024 09:42 |
Last Modified: |
05 Mar 2024 09:51 |
Publisher DOI: |
10.1038/s42003-024-05958-4 |
PubMed ID: |
38438709 |
BORIS DOI: |
10.48350/193791 |
URI: |
https://boris.unibe.ch/id/eprint/193791 |
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