Pathway analysis using random forests with bivariate node-split for survival outcomes
about
Sample size considerations of prediction-validation methods in high-dimensional data for survival outcomes.Integrative Pathway Analysis Using Graph-Based Learning with Applications to TCGA Colon and Ovarian DataRandom Effects Model for Multiple Pathway Analysis with Applications to Type II Diabetes Microarray DataRandom forests for genomic data analysis.Stratified pathway analysis to identify gene sets associated with oral contraceptive use and breast cancer.Pathway-based identification of SNPs predictive of survivalExtending information retrieval methods to personalized genomic-based studies of disease.Development and validation of a quantitative real-time polymerase chain reaction classifier for lung cancer prognosis.Big data and computational biology strategy for personalized prognosis.Gene selection using iterative feature elimination random forests for survival outcomes.Pathway hunting by random survival forests.Statistical aspect of translational and correlative studies in clinical trials.Survival Forests with R-Squared Splitting Rules.Leveraging external knowledge on molecular interactions in classification methods for risk prediction of patients.Automated QSAR with a Hierarchy of Global and Local Models.Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time-to-Event Analysis.Analysis of a large data set to identify predictors of blood transfusion in primary total hip and knee arthroplastyIntegration of gene interaction information into a reweighted random survival forest approach for accurate survival prediction and survival biomarker discovery
P2860
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P2860
Pathway analysis using random forests with bivariate node-split for survival outcomes
description
2009 nî lūn-bûn
@nan
2009 թուականի Նոյեմբերին հրատարակուած գիտական յօդուած
@hyw
2009 թվականի նոյեմբերին հրատարակված գիտական հոդված
@hy
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
name
Pathway analysis using random forests with bivariate node-split for survival outcomes
@ast
Pathway analysis using random forests with bivariate node-split for survival outcomes
@en
Pathway analysis using random forests with bivariate node-split for survival outcomes.
@nl
type
label
Pathway analysis using random forests with bivariate node-split for survival outcomes
@ast
Pathway analysis using random forests with bivariate node-split for survival outcomes
@en
Pathway analysis using random forests with bivariate node-split for survival outcomes.
@nl
prefLabel
Pathway analysis using random forests with bivariate node-split for survival outcomes
@ast
Pathway analysis using random forests with bivariate node-split for survival outcomes
@en
Pathway analysis using random forests with bivariate node-split for survival outcomes.
@nl
P2093
P2860
P356
P1433
P1476
Pathway analysis using random forests with bivariate node-split for survival outcomes
@en
P2093
Debayan Datta
Herbert Pang
Hongyu Zhao
P2860
P304
P356
10.1093/BIOINFORMATICS/BTP640
P407
P577
2009-11-18T00:00:00Z