Jacqueline S. Dron, Minxian Wang, et al.
Circulation: Genomic and Precision Medicine
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.
Jacqueline S. Dron, Minxian Wang, et al.
Circulation: Genomic and Precision Medicine
Toby G. Rossman, Ekaterina I. Goncharova, et al.
Mutation Research - Fundamental and Molecular Mechanisms of Mutagenesis
Angelique Cumbo, Patricia Agre, et al.
Cancer Practice
F. Parmigiani, E. Kay, et al.
Journal of Electron Spectroscopy and Related Phenomena