ArticleBMC bioinformatics2019
Challenges in proteogenomics: a comparison of analysis methods with the case study of the DREAM proteogenomics sub-challenge.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Multi-dataset Integration and Residual Connections Improve Proteome Prediction from Transcriptomes using Deep Learning.bioRxiv : the preprint server for biology · 2024Article
- Proteogenomics in Nephrology: A New Frontier in Nephrological Research.Current issues in molecular biology · 2024Review
- Protein prediction models support widespread post-transcriptional regulation of protein abundance by interacting partners.PLoS computational biology · 2022Article
- Deepening into Intracellular Signaling Landscape through Integrative Spatial Proteomics and Transcriptomics in a Lymphoma Model.Biomolecules · 2021Article
- Transcriptome features of striated muscle aging and predictability of protein level changes.Molecular omics · 2021Article
- Synthetic data generation with probabilistic Bayesian Networks.Mathematical biosciences and engineering : MBE · 2021Article
- The International Conference on Intelligent Biology and Medicine (ICIBM) 2019: bioinformatics methods and applications for human diseases.BMC bioinformatics · 2019Article
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6 authors.
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Abstract
backgroundProteomic measurements, which closely reflect phenotypes, provide insights into gene expression regulations and mechanisms underlying altered phenotypes. Further, integration of data on proteome and transcriptome levels can validate gene signatures associated with a phenotype. However, proteomic data is not as abundant as genomic data, and it is thus beneficial to use genomic features to predict protein abundances when matching proteomic samples or measurements within samples are lacking.
resultsWe evaluate and compare four data-driven models for prediction of proteomic data from mRNA measured in breast and ovarian cancers using the 2017 DREAM Proteogenomics Challenge data. Our results show that Bayesian network, random forests, LASSO, and fuzzy logic approaches can predict protein abundance levels with median ground truth-predicted correlation values between 0.2 and 0.5. However, the most accurately predicted proteins differ considerably between approaches.
conclusionsIn addition to benchmarking aforementioned machine learning approaches for predicting protein levels from transcript levels, we discuss challenges and potential solutions in state-of-the-art proteogenomic analyses.
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