about
A critical evaluation of the current "p-value controversy".ColoFinder: a prognostic 9-gene signature improves prognosis for 871 stage II and III colorectal cancer patients.Time-dependent and nonlinear effects of prognostic factors in nonmetastatic colorectal cancer.A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis.Predicting Prostate Cancer Recurrence After Radical Prostatectomy.Recursive Partitioning Method on Competing Risk Outcomes.CD49d prevails over the novel recurrent mutations as independent prognosticator of overall survival in chronic lymphocytic leukemia.Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models.Seven LncRNA-mRNA based risk score predicts the survival of head and neck squamous cell carcinomaRegulatory activity based risk model identifies survival of stage II and III colorectal carcinoma.Personalized Risk Prediction in Clinical Oncology Research: Applications and Practical Issues Using Survival Trees and Random Forests.A gene expression-based risk model reveals prognosis of gastric cancer.Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting.Tumor immunity and survival as a function of alternative neopeptides in human cancer.Radiomics as a Quantitative Imaging Biomarker: Practical Considerations and the Current Standpoint in Neuro-oncologic Studies.Relationships between circulating branched chain amino acid concentrations and risk of adverse cardiovascular events in patients with STEMI treated with PCI
P2860
Q30234457-10961C97-3F99-468D-8654-6C2C1E84125FQ36691267-AC052214-4F6F-4CD7-BACD-1EF50B2C51E6Q38679554-1A80B417-A2D0-4185-8AD2-18F7FAF7EB81Q38808665-A3254FFF-1F93-48AE-B71F-C1A0C58BD491Q39255593-3F34BF85-BCE8-4339-B906-F6E5D235BC2DQ39530377-F8740E0E-F461-4010-8EA0-B0E846CF6684Q40784167-8C875068-4682-418D-B832-EC2C3BFA216BQ41718329-0D8636E7-0A69-4424-B7E0-6F7AF743CDE1Q41823084-A7FA68EA-25A0-4CB9-AE47-7C49F2FF66A4Q47144679-0BC55CFF-EDC2-4ADD-B467-8728F2DC33CFQ47640517-397DB90E-6D28-4631-9757-F1C950FAB0DFQ49202365-022A1608-16A2-4BEB-A79D-8AADECA5DDCCQ49905853-5D7319B9-F4EA-4276-B8B2-00605F067635Q50020805-D0F99829-3F75-46AB-82E3-2CA1BA01EFB9Q53073424-46C03756-252E-474A-90B2-56C926D18805Q58563445-E3BE1EEE-98F6-48AE-9755-719A9A3DD6FA
P2860
description
2014 nî lūn-bûn
@nan
2014 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
Random survival forests for competing risks.
@ast
Random survival forests for competing risks.
@en
Random survival forests for competing risks.
@nl
type
label
Random survival forests for competing risks.
@ast
Random survival forests for competing risks.
@en
Random survival forests for competing risks.
@nl
prefLabel
Random survival forests for competing risks.
@ast
Random survival forests for competing risks.
@en
Random survival forests for competing risks.
@nl
P2093
P2860
P356
P1433
P1476
Random survival forests for competing risks.
@en
P2093
Bryan M Lau
Richard D Moore
Stephen J Gange
Thomas A Gerds
Udaya B Kogalur
P2860
P304
P356
10.1093/BIOSTATISTICS/KXU010
P577
2014-04-11T00:00:00Z