ArticlePloS one2013
An ensemble prognostic model for colorectal cancer.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Predicting major organ complications in primary Sjögren's disease using a machine learning ensemble strategy: a dual-center retrospective clinical study.Frontiers in immunology · 2026Article
- Computational Identification of Guillain-Barré Syndrome-Related Genes by an mRNA Gene Expression Profile and a Protein-Protein Interaction Network.Frontiers in molecular neuroscience · 2022Article
- Integration of gene expression data identifies key genes and pathways in colorectal cancer.Medical oncology (Northwood, London, England) · 2021Article
- Synergistic activation of the NEU4 promoter by p73 and AP2 in colon cancer cells.Scientific reports · 2019Article
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- Identification of Triple-Negative Breast Cancer Genes and a Novel High-Risk Breast Cancer Prediction Model Development Based on PPI Data and Support Vector Machines.Frontiers in genetics · 2019Article
- Cross talk of chromosome instability, CpG island methylator phenotype and mismatch repair in colorectal cancer.Oncology letters · 2018Article
- PACS-2 functions in colorectal cancer.Aging · 2018Article
- Cancer: Untethering Mitochondria from the Endoplasmic Reticulum?Frontiers in oncology · 2017Review
- Differentially expressed lncRNAs and mRNAs identified by microarray analysis in GBS patients vs healthy controls.Scientific reports · 2016Article
- Genetic differences among ethnic groups.BMC genomics · 2015Article
- Predicting DNA-binding sites of proteins based on sequential and 3D structural information.Molecular genetics and genomics : MGG · 2014Article
- Article
- Prediction and analysis of retinoblastoma related genes through gene ontology and KEGG.BioMed research international · 2013Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer can be grouped into Dukes A, B, C, and D stages based on its developments. Generally speaking, more advanced patients have poorer prognosis. To integrate progression stage prediction systems with recurrence prediction systems, we proposed an ensemble prognostic model for colorectal cancer. In this model, each patient was assigned a most possible stage and a most possible recurrence status. If a patient was predicted to be recurrence patient in advanced stage, he would be classified into high risk group. The ensemble model considered both progression stages and recurrence status. High risk patients and low risk patients predicted by the ensemble model had a significant different disease free survival (log-rank test p-value, 0.0016) and disease specific survival (log-rank test p-value, 0.0041). The ensemble model can better distinguish the high risk and low risk patients than the stage prediction model and the recurrence prediction model alone. This method could be applied to the studies of other diseases and it could significantly improve the prediction performance by ensembling heterogeneous information.
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