ReviewCancer genomics & proteomics
The Application of Bayesian Methods in Cancer Prognosis and Prediction.
Review in Cancer genomics & proteomics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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.
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
12 citing papers in PubMed.
- Factors influencing late HIV presentation in China: results from logistic regression and Bayesian network analyses.BMC infectious diseases · 2026Article
- Hallmarks of disease: how tuned hierarchies of intelligent molecular neural networks (MNNs) may matter.Endocrine-related cancer · 2026Review
- Beyond the blank page: Frequentist and Bayesian perspectives on risk prediction algorithms.World journal of gastrointestinal oncology · 2025Article
- TransMarker: Unveiling dynamic network biomarkers in cancer progression through cross-state graph alignment and optimal transport.PLoS computational biology · 2025Article
- AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis.Discover oncology · 2025Article
- Tutorial on survival modeling with applications to omics data.Bioinformatics (Oxford, England) · 2024Article
- Bayesian Approaches in Exploring Gene-environment and Gene-gene Interactions: A Comprehensive Review.Cancer genomics & proteomics · 2023Review
- A guide to multi-omics data collection and integration for translational medicine.Computational and structural biotechnology journal · 2023Review
- Bayesian Network Analysis for Prediction of Unplanned Hospital Readmissions of Cancer Patients with Breakthrough Cancer Pain and Complex Care Needs.Healthcare (Basel, Switzerland) · 2022Article
- Considerations of biomarker application for cancer continuum in the era of precision medicine.Current epidemiology reports · 2022Article
- From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer.International journal of molecular sciences · 2022Review
- Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology.Frontiers in molecular biosciences · 2022Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the development of high-throughput biological techniques, high-dimensional omics data have emerged. These molecular data provide a solid foundation for precision medicine and prognostic prediction of cancer. Bayesian methods contribute to constructing prognostic models with complex relationships in omics and improving performance by introducing different prior distribution, which is suitable for modelling the high-dimensional data involved. Using different omics, several Bayesian hierarchical approaches have been proposed for variable selection and model construction. In particular, the Bayesian methods of multi-omics integration have also been consistently proposed in recent years. Compared with single-omics, multi-omics integration modelling will contribute to improving predictive performance, gaining insights into the underlying mechanisms of tumour occurrence and development, and the discovery of more reliable biomarkers. In this work, we present a review of current proposed Bayesian approaches in prognostic prediction modelling in cancer.
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What OpenQuestion holds
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.