ArticleEBioMedicine2018
Module Analysis Captures Pancancer Genetically and Epigenetically Deregulated Cancer Driver Genes for Smoking and Antiviral Response.
Article in EBioMedicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 42 citations in OpenAlex.
- Clinical and Multiomic Features Differentiate Young Black and White Breast Cancer Cohorts Derived by Machine Learning Approaches.Clinical breast cancer · 2025Article
- From Genomic Exploration to Personalized Treatment: Next-Generation Sequencing in Oncology.Current issues in molecular biology · 2024Review
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- The Many Faces of Oligoadenylate Synthetases.Journal of interferon & cytokine research : the official journal of the International Society for Interferon and Cytokine Research · 2023Review
- Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers.Medical sciences (Basel, Switzerland) · 2023Article
- Microbial Synthesis of HemeMolecules (Basel, Switzerland) · 2023Review
- Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer.Cancer research · 2023Article
- Identifying key multifunctional components shared by critical cancer and normal liver pathways via SparseGMM.Cell reports methods · 2023Article
- SimiC enables the inference of complex gene regulatory dynamics across cell phenotypes.Communications biology · 2022Article
- Prospects and challenges of cancer systems medicine: from genes to disease networks.Briefings in bioinformatics · 2022Review
- Multi-Omics Profiling of the Tumor Microenvironment.Advances in experimental medicine and biology · 2022Article
- Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology.Frontiers in molecular biosciences · 2022Review
- Single-Cell Multiomics Techniques: From Conception to Applications.Frontiers in cell and developmental biology · 2022Review
- From DNA Copy Number Gains and Tumor Dependencies to Novel Therapeutic Targets for High-Risk Neuroblastoma.Journal of personalized medicine · 2021Review
- Integrated multi-omics analysis of ovarian cancer using variational autoencoders.Scientific reports · 2021Article
- A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research.International journal of molecular sciences · 2021Review
- Genomic data imputation with variational auto-encoders.GigaScience · 2020Article
- Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning-based neural network.GigaScience · 2020Article
- Imaging-AMARETTO: An Imaging Genomics Software Tool to Interrogate Multiomics Networks for Relevance to Radiography and Histopathology Imaging Biomarkers of Clinical Outcomes.JCO clinical cancer informatics · 2020Article
- Circulating Cell-Free Nucleic Acids as Epigenetic Biomarkers in Precision Medicine.Frontiers in genetics · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
The availability of increasing volumes of multi-omics profiles across many cancers promises to improve our understanding of the regulatory mechanisms underlying cancer. The main challenge is to integrate these multiple levels of omics profiles and especially to analyze them across many cancers. Here we present AMARETTO, an algorithm that addresses both challenges in three steps. First, AMARETTO identifies potential cancer driver genes through integration of copy number, DNA methylation and gene expression data. Then AMARETTO connects these driver genes with co-expressed target genes that they control, defined as regulatory modules. Thirdly, we connect AMARETTO modules identified from different cancer sites into a pancancer network to identify cancer driver genes. Here we applied AMARETTO in a pancancer study comprising eleven cancer sites and confirmed that AMARETTO captures hallmarks of cancer. We also demonstrated that AMARETTO enables the identification of novel pancancer driver genes. In particular, our analysis led to the identification of pancancer driver genes of smoking-induced cancers and 'antiviral' interferon-modulated innate immune response. SOFTWARE AVAILABILITY: AMARETTO is available as an R package at https://bitbucket.org/gevaertlab/pancanceramaretto.
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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.