Angelique Cumbo, Patricia Agre, et al.
Cancer Practice
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.
Angelique Cumbo, Patricia Agre, et al.
Cancer Practice
F.J. Himpsel
Journal of Electron Spectroscopy and Related Phenomena
Daniel Alexander Ford, James H. Kaufman, et al.
International Journal of Health Geographics
Niall P. Hardy, Pol Mac Aonghusa, et al.
Surgical Endoscopy