ArticleInternational journal of molecular sciences2022
Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 20 citations in OpenAlex.
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Metab8D: a metabolic regulome network from multiomics and machine learning.Communications biology · 2026Article
- UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.Nature cancer · 2025Article
- Transcriptomic insights into the interplay between polyketide biosynthesis and other secondary metabolite biosynthetic clusters and biological pathways in entomopathogenFrontiers in microbiology · 2025Article
- Review
- TransCell: In Silico Characterization of Genomic Landscape and Cellular Responses by Deep Transfer Learning.Genomics, proteomics & bioinformatics · 2024Article
- Transcriptional and metabolic effects of aspartate-glutamate carrier isoform 1 (AGC1) downregulation in mouse oligodendrocyte precursor cells (OPCs).Cellular & molecular biology letters · 2024Article
- Applications of multi-omics analysis in human diseases.MedComm · 2023Review
- Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
The Metabolome and Transcriptome are mutually communicating within cancer cells, and this interplay is translated into the existence of quantifiable correlation structures between gene expression and metabolite abundance levels. Studying these correlations could provide a novel venue of understanding cancer and the discovery of novel biomarkers and pharmacological strategies, as well as laying the foundation for the prediction of metabolite quantities by leveraging information from the more widespread transcriptomics data. In the current paper, we investigate the correlation between gene expression and metabolite levels in the Cancer Cell Line Encyclopedia dataset, building a direct correlation network between the two molecular ensembles. We show that a metabolite/transcript correlation network can be used to predict metabolite levels in different samples and datasets, such as the NCI-60 cancer cell line dataset, both on a sample-by-sample basis and in differential contrasts. We also show that metabolite levels can be predicted in principle on any sample and dataset for which transcriptomics data are available, such as the Cancer Genome Atlas (TCGA).
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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.