ArticleSpringerPlus2016
Random rotation survival forest for high dimensional censored data.
Article in SpringerPlus, 2016. 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.
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Who cites it
12 citing papers in PubMed.
- Machine Learning Model Predicts Monoclonal Gammopathy Using Routine Laboratory Values.JCO clinical cancer informatics · 2026Article
- Novel Genomic Biomarkers Improve Post-Hematopoietic Cell Transplantation Relapse Risk Stratification for Patients With Myelodysplastic Syndromes.JCO precision oncology · 2026Article
- Machine learning-based prognostic model for metastatic breast cancer and its interpretability: a multicenter retrospective study.Gland surgery · 2025Article
- Machine Learning Model Predicts Abnormal Lymphocytosis Associated With Chronic Lymphocytic Leukemia.JCO clinical cancer informatics · 2025Observational
- Machine learning-based prediction of disease-free survival in breast cancer patients with non-pathological complete response after neoadjuvant chemotherapy: a retrospective multicenter cohort study.American journal of cancer research · 2025Article
- Machine learning progressive CKD risk prediction model is associated with CKD-mineral bone disorder.Bone reports · 2024Article
- Accelerated and Interpretable Oblique Random Survival Forests.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- CKD Progression Prediction in a Diverse US Population: A Machine-Learning Model.Kidney medicine · 2023Article
- Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma.Scientific reports · 2023Article
- Development and validation of a novel necroptosis-related gene signature for predicting prognosis and therapeutic response in Ewing sarcoma.Frontiers in medicine · 2023Article
- Identifying Stage II Colorectal Cancer Recurrence Associated Genes by Microarray Meta-Analysis and Building Predictive Models with Machine Learning Algorithms.Journal of oncology · 2021Article
- A Selective Review on Random Survival Forests for High Dimensional Data.Quantitative bio-science · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, rotation forest has been extended to regression and survival analysis problems. However, due to intensive computation incurred by principal component analysis, rotation forest often fails when high-dimensional or big data are confronted. In this study, we extend rotation forest to high dimensional censored time-to-event data analysis by combing random subspace, bagging and rotation forest. Supported by proper statistical analysis, we show that the proposed method random rotation survival forest outperforms state-of-the-art survival ensembles such as random survival forest and popular regularized Cox models.
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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.