ArticleProceedings of the National Academy of Sciences of the United States of America2021
Protein structure-based gene expression signatures.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Capturing Unanticipated Drug Toxicities Using an Ensemble Machine Learning Approach.Research square · 2025Article
- Review
- Structural analysis of genomic and proteomic signatures reveal dynamic expression of intrinsically disordered regions in breast cancer.iScience · 2024Article
- Biomarkers for immune checkpoint inhibition in sarcomas - are we close to clinical implementation?Biomarker research · 2023Review
- Structural signatures: a web server for exploring a database of and generating protein structural features from human cell lines and tissues.Database : the journal of biological databases and curation · 2022Article
- Sex-dependent transcription of cardiac electrophysiology and links to acetylation modifiers based on the GTEx database.Frontiers in cardiovascular medicine · 2022Article
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Authors and funding
10 authors.
Funding
Abstract
Gene expression signatures (GES) connect phenotypes to differential messenger RNA (mRNA) expression of genes, providing a powerful approach to define cellular identity, function, and the effects of perturbations. The use of GES has suffered from vague assessment criteria and limited reproducibility. Because the structure of proteins defines the functional capability of genes, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from multiple levels of protein structure (e.g., domain and fold) encoded by the mRNAs in GES. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), the all RNA-seq and ChIP-seq sample and signature search (ARCHS4) database, and mRNA expression of drug effects on cardiomyocytes show that sGES are useful for characterizing biological phenomena. sGES enable phenotypic characterization across experimental platforms, facilitates interoperability of expression datasets, and describe drug action on cells.
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Registered trials
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