ArticleScientific reports2019
Uncovering biomarker genes with enriched classification potential from Hallmark gene sets.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
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- GEMDiff: a diffusion workflow bridges between normal and tumor gene expression states: a breast cancer case study.Briefings in bioinformatics · 2025Article
- GSFM: A genome-scale functional module transformation to represent drug efficacy forActa pharmaceutica Sinica. B · 2025Article
- DeepGSEA: explainable deep gene set enrichment analysis for single-cell transcriptomic data.Bioinformatics (Oxford, England) · 2024Article
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- Simulating the restoration of normal gene expression from different thyroid cancer stages using deep learning.BMC cancer · 2022Article
- Identification of condition-specific regulatory mechanisms in normal and cancerous human lung tissue.BMC genomics · 2022Article
- Transfer transcriptomic signatures for infectious diseases.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Ciliary extracellular vesicles are distinct from the cytosolic extracellular vesicles.Journal of extracellular vesicles · 2021Article
- Diagnostic blood RNA profiles for human acute spinal cord injury.The Journal of experimental medicine · 2021Article
- A Comprehensive Risk Assessment and Stratification Model of Papillary Thyroid Carcinoma Based on the Autophagy-Related LncRNAs.Frontiers in oncology · 2021Article
- Exploration into biomarker potential of region-specific brain gene co-expression networks.Scientific reports · 2020Article
- The Landscape of Iron Metabolism-Related and Methylated Genes in the Prognosis Prediction of Clear Cell Renal Cell Carcinoma.Frontiers in oncology · 2020Article
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5 authors.
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Abstract
Given the complex relationship between gene expression and phenotypic outcomes, computationally efficient approaches are needed to sift through large high-dimensional datasets in order to identify biologically relevant biomarkers. In this report, we describe a method of identifying the most salient biomarker genes in a dataset, which we call "candidate genes", by evaluating the ability of gene combinations to classify samples from a dataset, which we call "classification potential". Our algorithm, Gene Oracle, uses a neural network to test user defined gene sets for polygenic classification potential and then uses a combinatorial approach to further decompose selected gene sets into candidate and non-candidate biomarker genes. We tested this algorithm on curated gene sets from the Molecular Signatures Database (MSigDB) quantified in RNAseq gene expression matrices obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) data repositories. First, we identified which MSigDB Hallmark subsets have significant classification potential for both the TCGA and GTEx datasets. Then, we identified the most discriminatory candidate biomarker genes in each Hallmark gene set and provide evidence that the improved biomarker potential of these genes may be due to reduced functional complexity.
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