Mani Abedini, Michael Kirley, et al.
Australasian Medical Journal
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.
Mani Abedini, Michael Kirley, et al.
Australasian Medical Journal
D.E. Eastman, J.J. Donelon, et al.
Nuclear Instruments and Methods
Shengping Liu, Baoyao Zhou, et al.
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Colm T. Whelan, R.K. Nesbet, et al.
Journal of Electron Spectroscopy and Related Phenomena