ArticleNucleic acids research2019
Quantifying gene selection in cancer through protein functional alteration bias.
Article in Nucleic acids research, 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.
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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, 32 citations in OpenAlex.
- Integrative machine learning approach to risk prediction for dementia and Alzheimer's disease.GeroScience · 2026Article
- LncRNA SNHG3: a potential biomarker for human diseases.Frontiers in cell and developmental biology · 2026Review
- Integrative Gene-Centric Analysis Reveals Cellular Pathways Associated with Heritable Breast Cancer Predisposition.Cancers · 2025Article
- Multiome-wide Association Studies: Novel Approaches for Understanding Diseases.Genomics, proteomics & bioinformatics · 2024Review
- PWAS Hub for exploring gene-based associations of common complex diseases.Genome research · 2024Article
- Post-implantation analysis of genomic variations in the progeny from developing fetus to birth.Human genomics · 2024Article
- Discovering predisposing genes for hereditary breast cancer using deep learning.Briefings in bioinformatics · 2024Article
- Revealing the genetic complexity of hypothyroidism: integrating complementary association methods.Frontiers in genetics · 2024Article
- Gene-based association study reveals a distinct female genetic signal in primary hypertension.Human genetics · 2023Article
- Evolutionary and functional lessons from human-specific amino acid substitution matrices.NAR genomics and bioinformatics · 2021Article
- PWAS: proteome-wide association study-linking genes and phenotypes by functional variation in proteins.Genome biology · 2020Article
- Serine substitutions are linked to codon usage and differ for variable and conserved protein regions.Scientific reports · 2019Article
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Compiling the catalogue of genes actively involved in cancer is an ongoing endeavor, with profound implications to the understanding and treatment of the disease. An abundance of computational methods have been developed to screening the genome for candidate driver genes based on genomic data of somatic mutations in tumors. Existing methods make many implicit and explicit assumptions about the distribution of random mutations. We present FABRIC, a new framework for quantifying the selection of genes in cancer by assessing the effects of de-novo somatic mutations on protein-coding genes. Using a machine-learning model, we quantified the functional effects of ∼3M somatic mutations extracted from over 10 000 human cancerous samples, and compared them against the effects of all possible single-nucleotide mutations in the coding human genome. We detected 593 protein-coding genes showing statistically significant bias towards harmful mutations. These genes, discovered without any prior knowledge, show an overwhelming overlap with known cancer genes, but also include many overlooked genes. FABRIC is designed to avoid false discoveries by comparing each gene to its own background model using rigorous statistics, making minimal assumptions about the distribution of random somatic mutations. The framework is an open-source project with a simple command-line interface.
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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.