ArticleProteomics2020
Using Deep Learning to Extrapolate Protein Expression Measurements.
Article in Proteomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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Who cites it
5 citing papers in PubMed, 14 citations in OpenAlex.
- PEPerMINT: peptide abundance imputation in mass spectrometry-based proteomics using graph neural networks.Bioinformatics (Oxford, England) · 2024Article
- Integrated View of Baseline Protein Expression in Human Tissues.Journal of proteome research · 2023Article
- Experimental reproducibility limits the correlation between mRNA and protein abundances in tumor proteomic profiles.Cell reports methods · 2022Article
- Predicting missing proteomics values using machine learning: Filling the gap using transcriptomics and other biological features.Computational and structural biotechnology journal · 2022Article
- Using Deep Learning to Extrapolate Protein Expression Measurements.Proteomics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 4 institutions in 3 countries.
Funding
Abstract
Mass spectrometry (MS)-based quantitative proteomics experiments typically assay a subset of up to 60% of the ≈20 000 human protein coding genes. Computational methods for imputing the missing values using RNA expression data usually allow only for imputations of proteins measured in at least some of the samples. In silico methods for comprehensively estimating abundances across all proteins are still missing. Here, a novel method is proposed using deep learning to extrapolate the observed protein expression values in label-free MS experiments to all proteins, leveraging gene functional annotations and RNA measurements as key predictive attributes. This method is tested on four datasets, including human cell lines and human and mouse tissues. This method predicts the protein expression values with average
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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.