ArticleCancer informatics2020
A Pan-Cancer and Polygenic Bayesian Hierarchical Model for the Effect of Somatic Mutations on Survival.
Article in Cancer informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Bayesian Transfer Learning.Statistical science : a review journal of the Institute of Mathematical Statistics · 2025Article
- Pan-cancer predictive survival model development and evaluation using electronic health record and genetic data across 10 cancer types.Discover oncology · 2025Article
- Prognostic determinants in cancer survival: a multidimensional evaluation of clinical and genetic factors across 10 cancer types in the participants of Genomics England's 100,000 Genomes Project.Discover oncology · 2024Article
- A hierarchical spike-and-slab model for pan-cancer survival using pan-omic data.BMC bioinformatics · 2022Article
- The Application of Bayesian Methods in Cancer Prognosis and Prediction.Cancer genomics & proteomicsReview
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
We built a novel Bayesian hierarchical survival model based on the somatic mutation profile of patients across 50 genes and 27 cancer types. The pan-cancer quality allows for the model to "borrow" information across cancer types, motivated by the assumption that similar mutation profiles may have similar (but not necessarily identical) effects on survival across different tissues of origin or tumor types. The effect of a mutation at each gene was allowed to vary by cancer type, whereas the mean effect of each gene was shared across cancers. Within this framework, we considered 4 parametric survival models (normal, log-normal, exponential, and Weibull), and we compared their performance via a cross-validation approach in which we fit each model on training data and estimate the log-posterior predictive likelihood on test data. The log-normal model gave the best fit, and we investigated the partial effect of each gene on survival via a forward selection procedure. Through this we determined that mutations at
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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.