ArticleFrontiers in genetics2019
Pan-Cancer and Single-Cell Modeling of Genomic Alterations Through Gene Expression.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Article
- Single-Cell Sequencing Identifies Master Regulators Affected by Panobinostat in Neuroblastoma Cells.Genes · 2022Article
- Article
- The R Language: An Engine for Bioinformatics and Data Science.Life (Basel, Switzerland) · 2022Review
- Analysis of Yes-Associated Protein-1 (YAP1) Target Gene Signature to Predict Progressive Breast Cancer.Journal of clinical medicine · 2022Article
- Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines.International journal of molecular sciences · 2022Article
- Single-Cell Gene Network Analysis and Transcriptional Landscape of MYCN-Amplified Neuroblastoma Cell Lines.Biomolecules · 2021Article
- Evidence of Omics, Immune Infiltration, and Pharmacogenomic for SENP1 in the Pan-Cancer Cohort.Frontiers in pharmacology · 2021Article
- Review
- Master Regulator Analysis of the SARS-CoV-2/Human Interactome.Journal of clinical medicine · 2020Article
- Pan-Cancer and Single-Cell Modeling of Genomic Alterations Through Gene Expression.Frontiers in genetics · 2019Article
- Pan-cancer multi-omics analysis to identify the potential pro-oncogenic properties of GREM1 as a promising targets for cancer prognosis and therapeutics.International journal of immunopathology and pharmacologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is a disease often characterized by the presence of multiple genomic alterations, which trigger altered transcriptional patterns and gene expression, which in turn sustain the processes of tumorigenesis, tumor progression, and tumor maintenance. The links between genomic alterations and gene expression profiles can be utilized as the basis to build specific molecular tumorigenic relationships. In this study, we perform pan-cancer predictions of the presence of single somatic mutations and copy number variations using machine learning approaches on gene expression profiles. We show that gene expression can be used to predict genomic alterations in every tumor type, where some alterations are more predictable than others. We propose gene aggregation as a tool to improve the accuracy of alteration prediction models from gene expression profiles. Ultimately, we show how this principle can be beneficial in intrinsically noisy datasets, such as those based on single-cell sequencing.
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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.