ArticleHuman genomics2018
Bayesian variable selection for parametric survival model with applications to cancer omics data.
Article in Human genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data.Bioinformatics (Oxford, England) · 2023Article
- Bayesian penalized Buckley-James method for high dimensional bivariate censored regression models.Lifetime data analysis · 2022Article
- Integration of immune and hypoxia gene signatures improves the prediction of radiosensitivity in breast cancer.American journal of cancer research · 2022Article
- Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis.Nature communications · 2021Article
- The Application of Bayesian Methods in Cancer Prognosis and Prediction.Cancer genomics & proteomicsReview
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Authors and funding
7 authors.
Funding
Abstract
backgroundModeling thousands of markers simultaneously has been of great interest in testing association between genetic biomarkers and disease or disease-related quantitative traits. Recently, an expectation-maximization (EM) approach to Bayesian variable selection (EMVS) facilitating the Bayesian computation was developed for continuous or binary outcome using a fast EM algorithm. However, it is not suitable to the analyses of time-to-event outcome in many public databases such as The Cancer Genome Atlas (TCGA).
resultsWe extended the EMVS to high-dimensional parametric survival regression framework (SurvEMVS). A variant of cyclic coordinate descent (CCD) algorithm was used for efficient iteration in M-step, and the extended Bayesian information criteria (EBIC) was employed to make choice on hyperparameter tuning. We evaluated the performance of SurvEMVS using numeric simulations and illustrated the effectiveness on two real datasets. The results of numerical simulations and two real data analyses show the well performance of SurvEMVS in aspects of accuracy and computation. Some potential markers associated with survival of lung or stomach cancer were identified.
conclusionsThese results suggest that our model is effective and can cope with high-dimensional omics data.
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