ArticleGenetics2017
Pathway-Structured Predictive Model for Cancer Survival Prediction: A Two-Stage Approach.
Article in Genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables.Briefings in bioinformatics · 2025Review
- Construct prognostic models of multiple myeloma with pathway information incorporated.PLoS computational biology · 2024Article
- A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data.BMC medical research methodology · 2024Article
- A non-negative spike-and-slab lasso generalized linear stacking prediction modeling method for high-dimensional omics data.BMC bioinformatics · 2024Article
- DGSurv: Dynamic Graph-Based Multimodal Learning for Interpretable Cancer Survival Prediction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Article
- Novel Application of Survival Models for Predicting Microbial Community Transitions with Variable Selection for Environmental DNA.Applied and environmental microbiology · 2022Article
- Learning-based Cancer Treatment Outcome Prognosis using Multimodal Biomarkers.IEEE transactions on radiation and plasma medical sciences · 2022Article
- A Novel Prognostic Index Model for Adult Hemophagocytic Lymphohistiocytosis: A Multicenter Retrospective Analysis in China.Frontiers in immunology · 2022Article
- Explore association of genes in PDL1/PD1 pathway to radiotherapy survival benefit based on interaction model strategy.Radiation oncology (London, England) · 2021Article
- Pan-cancer evaluation of gene expression and somatic alteration data for cancer prognosis prediction.BMC cancer · 2021Article
- A potential prognostic prediction model of colon adenocarcinoma with recurrence based on prognostic lncRNA signatures.Human genomics · 2020Article
- A novel risk score model based on eight genes and a nomogram for predicting overall survival of patients with osteosarcoma.BMC cancer · 2020Article
- Comparison of pathway and gene-level models for cancer prognosis prediction.BMC bioinformatics · 2020Article
- Deep learning-based model for predicting progression in patients with head and neck squamous cell carcinoma.Cancer biomarkers : section A of Disease markers · 2020Article
- Definition of a novel vascular invasion-associated multi-gene signature for predicting survival in patients with hepatocellular carcinoma.Oncology letters · 2020Article
- Deep learning with multimodal representation for pancancer prognosis prediction.Bioinformatics (Oxford, England) · 2019Article
- BhGLM: Bayesian hierarchical GLMs and survival models, with applications to genomics and epidemiology.Bioinformatics (Oxford, England) · 2019Article
- Tightly integrated genomic and epigenomic data mining using tensor decomposition.Bioinformatics (Oxford, England) · 2019Article
- Toward the precision breast cancer survival prediction utilizing combined whole genome-wide expression and somatic mutation analysis.BMC medical genomics · 2018Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Heterogeneity in terms of tumor characteristics, prognosis, and survival among cancer patients has been a persistent problem for many decades. Currently, prognosis and outcome predictions are made based on clinical factors and/or by incorporating molecular profiling data. However, inaccurate prognosis and prediction may result by using only clinical or molecular information directly. One of the main shortcomings of past studies is the failure to incorporate prior biological information into the predictive model, given strong evidence of the pathway-based genetic nature of cancer, i.e., the potential for oncogenes to be grouped into pathways based on biological functions such as cell survival, proliferation, and metastatic dissemination. To address this problem, we propose a two-stage approach to incorporate pathway information into the prognostic modeling using large-scale gene expression data. In the first stage, we fit all predictors within each pathway using the penalized Cox model and Bayesian hierarchical Cox model. In the second stage, we combine the cross-validated prognostic scores of all pathways obtained in the first stage as new predictors to build an integrated prognostic model for prediction. We apply the proposed method to analyze two independent breast and ovarian cancer datasets from The Cancer Genome Atlas (TCGA), predicting overall survival using large-scale gene expression profiling data. The results from both datasets show that the proposed approach not only improves survival prediction compared with the alternative analyses that ignore the pathway information, but also identifies significant biological pathways.
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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.