ArticleInterface focus2013
Identification of ovarian cancer driver genes by using module network integration of multi-omics data.
Article in Interface focus, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 57 citations in OpenAlex.
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- A genomic perspective of the aging human and mouse lung with a focus on immune response and cellular senescence.Immunity & ageing : I & A · 2023Article
- Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer.Cancer research · 2023Article
- Multimodal data fusion for cancer biomarker discovery with deep learning.Nature machine intelligence · 2023Article
- Identifying key multifunctional components shared by critical cancer and normal liver pathways via SparseGMM.Cell reports methods · 2023Article
- Prospects and challenges of cancer systems medicine: from genes to disease networks.Briefings in bioinformatics · 2022Review
- Network Pharmacology and Molecular Docking Validation to Reveal the Pharmacological Mechanisms of Kangai Injection against Colorectal Cancer.BioMed research international · 2022Article
- Identifying Disease Related Genes by Network Representation and Convolutional Neural Network.Frontiers in cell and developmental biology · 2021Article
- 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
- DriverDBv3: a multi-omics database for cancer driver gene research.Nucleic acids research · 2020Article
- Monitoring methylation-driven genes as prognostic biomarkers in patients with lung squamous cell cancer.Oncology letters · 2020Article
- Deep learning with multimodal representation for pancancer prognosis prediction.Bioinformatics (Oxford, England) · 2019Article
- PI3K-AKT-mTOR and NFκB Pathways in Ovarian Cancer: Implications for Targeted Therapeutics.Cancers · 2019Review
- The impact of DNA methylation on the cancer proteome.PLoS computational biology · 2019Article
- Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes.EBioMedicine · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
The increasing availability of multi-omics cancer datasets has created a new opportunity for data integration that promises a more comprehensive understanding of cancer. The challenge is to develop mathematical methods that allow the integration and extraction of knowledge from large datasets such as The Cancer Genome Atlas (TCGA). This has led to the development of a variety of omics profiles that are highly correlated with each other; however, it remains unknown which profile is the most meaningful and how to efficiently integrate different omics profiles. We developed AMARETTO, an algorithm to identify cancer drivers by integrating a variety of omics data from cancer and normal tissue. AMARETTO first models the effects of genomic/epigenomic data on disease-specific gene expression. AMARETTO's second step involves constructing a module network to connect the cancer drivers with their downstream targets. We observed that more gene expression variation can be explained when using disease-specific gene expression data. We applied AMARETTO to the ovarian cancer TCGA data and identified several cancer driver genes of interest, including novel genes in addition to known drivers of cancer. Finally, we showed that certain modules are predictive of good versus poor outcome, and the associated drivers were related to DNA repair pathways.
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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.