ArticleCommunications biology2024
ScLinear predicts protein abundance at single-cell resolution.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
6 citing papers in PubMed.
- DPAS-Graph: adaptive spatial-feature relation learning for spatial RNA-to-protein prediction and virtual protein profiling.Briefings in bioinformatics · 2026Article
- Towards the construction of a virtual yeast.Nature · 2026Review
- DGAT: a dual-graph attention network for inferring spatial protein landscapes from transcriptomics.Nature communications · 2026Article
- Local transcriptional covariation produces accurate estimates of cell phenotype.Bioinformatics (Oxford, England) · 2026Article
- Machine learning predictions surpass individual mRNAs as a proxy of single-cell protein expression.Genome biology · 2026Article
- SR2P: an efficient stacking method to predict protein abundance from gene expression in spatial transcriptomics data.bioRxiv : the preprint server for biology · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.