ArticleBMC biology2019
Joint learning improves protein abundance prediction in cancers.
Article in BMC biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 27 citations in OpenAlex.
- T2Pdecoder enables protein-centric analyses from transcriptomic data.Nature communications · 2026Article
- Inferring protein from transcript abundances using convolutional neural networks.BioData mining · 2025Article
- Multi-dataset Integration and Residual Connections Improve Proteome Prediction from Transcriptomes using Deep Learning.bioRxiv : the preprint server for biology · 2024Article
- Predicting single-cell cellular responses to perturbations using cycle consistency learning.Bioinformatics (Oxford, England) · 2024Article
- Predicting locus-specific DNA methylation levels in cancer and paracancer tissues.Epigenomics · 2024Article
- Extrapolated cross-validation for randomized ensembles.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- PARROT: Prediction of enzyme abundances using protein-constrained metabolic models.PLoS computational biology · 2023Article
- Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery.Proteomes · 2023Review
- Protein prediction models support widespread post-transcriptional regulation of protein abundance by interacting partners.PLoS computational biology · 2022Article
- Experimental reproducibility limits the correlation between mRNA and protein abundances in tumor proteomic profiles.Cell reports methods · 2022Article
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Transcriptome features of striated muscle aging and predictability of protein level changes.Molecular omics · 2021Article
- Using Deep Learning to Extrapolate Protein Expression Measurements.Proteomics · 2020Article
- Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology.Frontiers in pharmacology · 2020Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe classic central dogma in biology is the information flow from DNA to mRNA to protein, yet complicated regulatory mechanisms underlying protein translation often lead to weak correlations between mRNA and protein abundances. This is particularly the case in cancer samples and when evaluating the same gene across multiple samples.
resultsHere, we report a method for predicting proteome from transcriptome, using a training dataset provided by NCI-CPTAC and TCGA, consisting of transcriptome and proteome data from 77 breast and 105 ovarian cancer samples. First, we establish a generic model capturing the correlation between mRNA and protein abundance of a single gene. Second, we build a gene-specific model capturing the interdependencies among multiple genes in a regulatory network. Third, we create a cross-tissue model by joint learning the information of shared regulatory networks and pathways across cancer tissues. Our method ranked first in the NCI-CPTAC DREAM Proteogenomics Challenge, and the predictive performance is close to the accuracy of experimental replicates. Key functional pathways and network modules controlling the proteomic abundance in cancers were revealed, in particular metabolism-related genes.
conclusionsWe present a method to predict proteome from transcriptome, leveraging data from different cancer tissues to build a trans-tissue model, and suggest how to integrate information from multiple cancers to provide a foundation for further research.
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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 Socratic graph.