ArticleGenetics2016
Increased Proportion of Variance Explained and Prediction Accuracy of Survival of Breast Cancer Patients with Use of Whole-Genome Multiomic Profiles.
Article in Genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Priority-Elastic net for binary disease outcome prediction based on multi-omics data.BioData mining · 2024Article
- Relative importance of genotype, gene expression, and DNA methylation on complex traits in perennial ryegrass.The plant genome · 2022Article
- Integration of DNA Methylation and Transcriptome Data Improves Complex Trait Prediction inPlants (Basel, Switzerland) · 2022Article
- MOSS: multi-omic integration with sparse value decomposition.Bioinformatics (Oxford, England) · 2022Article
- Epigenome-wide contributions to individual differences in childhood phenotypes: a GREML approach.Clinical epigenetics · 2022Article
- Incorporating Omics Data in Genomic Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Incorporation of Trait-Specific Genetic Information into Genomic Prediction Models.Methods in molecular biology (Clifton, N.J.) · 2022Article
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- Integrating genomic and infrared spectral data improves the prediction of milk protein composition in dairy cattle.Genetics, selection, evolution : GSE · 2021Article
- Leveraging Multiple Layers of Data To PredictG3 (Bethesda, Md.) · 2020Article
- Multi-omics-data-assisted genomic feature markers preselection improves the accuracy of genomic prediction.Journal of animal science and biotechnology · 2020Article
- Xylem systems genetics analysis reveals a key regulator of lignin biosynthesis inGenome research · 2020Article
- eBreCaP: extreme learning-based model for breast cancer survival prediction.IET systems biology · 2020Article
- Multi-omic signatures identify pan-cancer classes of tumors beyond tissue of origin.Scientific reports · 2020Article
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- A plea for taking all available clinical information into account when assessing the predictive value of omics data.BMC medical research methodology · 2019Article
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7 authors.
Funding
Abstract
Whole-genome multiomic profiles hold valuable information for the analysis and prediction of disease risk and progression. However, integrating high-dimensional multilayer omic data into risk-assessment models is statistically and computationally challenging. We describe a statistical framework, the Bayesian generalized additive model ((BGAM), and present software for integrating multilayer high-dimensional inputs into risk-assessment models. We used BGAM and data from The Cancer Genome Atlas for the analysis and prediction of survival after diagnosis of breast cancer. We developed a sequence of studies to (1) compare predictions based on single omics with those based on clinical covariates commonly used for the assessment of breast cancer patients (COV), (2) evaluate the benefits of combining COV and omics, (3) compare models based on (a) COV and gene expression profiles from oncogenes with (b) COV and whole-genome gene expression (WGGE) profiles, and (4) evaluate the impacts of combining multiple omics and their interactions. We report that (1) WGGE profiles and whole-genome methylation (METH) profiles offer more predictive power than any of the COV commonly used in clinical practice (e.g., subtype and stage), (2) adding WGGE or METH profiles to COV increases prediction accuracy, (3) the predictive power of WGGE profiles is considerably higher than that based on expression from large-effect oncogenes, and (4) the gain in prediction accuracy when combining multiple omics is consistent. Our results show the feasibility of omic integration and highlight the importance of WGGE and METH profiles in breast cancer, achieving gains of up to 7 points area under the curve (AUC) over the COV in some cases.
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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.