Optimally splitting cases for training and testing high dimensional classifiers
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A methodology for exploring biomarker--phenotype associations: application to flow cytometry data and systemic sclerosis clinical manifestationsAn Efficient Approach to Screening Epigenome-Wide Data.Clinical prediction rule for delayed hemothorax after minor thoracic injury: a multicentre derivation and validation study.Two-stage adaptive cutoff design for building and validating a prognostic biomarker signatureRiGoR: reporting guidelines to address common sources of bias in risk model development.Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration.Three-gene predictor of clinical outcome for gastric cancer patients treated with chemotherapy.Assessing urinary flow rate, creatinine, osmolality and other hydration adjustment methods for urinary biomonitoring using NHANES arsenic, iodine, lead and cadmium dataMultivariate analysis of the volumetric capnograph for PaCO2 estimationSample size requirements for training high-dimensional risk predictors.Machine learning-enabled discovery and design of membrane-active peptides.Issues in developing multivariable molecular signatures for guiding clinical care decisions.Authentication of Smartphone Users Based on Activity Recognition and Mobile Sensing.Integration of RNA-Seq data with heterogeneous microarray data for breast cancer profiling.Enhancement of the adaptive signature design for learning and confirming in a single pivotal trial.Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables.
P2860
Q30993697-C326A51E-EAE5-4673-A466-6566CF279291Q31066390-080640A0-0812-4887-AD85-B9603E7093D9Q33875137-BF0418EE-53BC-402E-B9D1-816DA6EE1D23Q34490312-0BDA4975-8E66-4348-B4C8-CDBB09976454Q35033342-9604DEB2-7EC4-442F-BE83-B2FA306E636CQ35042433-A4F07126-30B6-4908-ABC2-E0098F8E792AQ35875892-5EE1F7B4-A44B-4B66-826A-AF3C9AC73BEFQ36048730-234F2AC5-301D-4AD6-85F4-A9A5BF7E3F1CQ36204989-52A4C86E-E25A-4F29-AE49-CEBECF0ED659Q37162810-0FE5765B-097E-47F2-BBBB-499FDC83908BQ39448556-3B1D8E32-6CAD-47B6-A839-B2A44B26FB55Q39454048-477502BA-BAC8-48D0-A6B6-ED9C88EF3D8AQ42140598-EE9BCD1D-6EDC-4412-9AE3-E4B746BA9555Q47094727-8799810E-FFE9-47A0-9286-0F41CEE9487EQ48187708-3D45C32C-9D80-400D-9885-D167B9F1FEDBQ54966148-3A1F7D31-81DA-4FB8-A2D9-2AD4805897A6
P2860
Optimally splitting cases for training and testing high dimensional classifiers
description
2011 nî lūn-bûn
@nan
2011 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
Optimally splitting cases for training and testing high dimensional classifiers
@ast
Optimally splitting cases for training and testing high dimensional classifiers
@en
type
label
Optimally splitting cases for training and testing high dimensional classifiers
@ast
Optimally splitting cases for training and testing high dimensional classifiers
@en
prefLabel
Optimally splitting cases for training and testing high dimensional classifiers
@ast
Optimally splitting cases for training and testing high dimensional classifiers
@en
P2860
P356
P1433
P1476
Optimally splitting cases for training and testing high dimensional classifiers
@en
P2093
Kevin K Dobbin
Richard M Simon
P2860
P2888
P356
10.1186/1755-8794-4-31
P577
2011-04-08T00:00:00Z
P5875
P6179
1013060970