Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.
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Posttraumatic Stress Disorder, Adverse Childhood Events, and Buccal Cell Telomere Length in Elderly Swiss Former Indentured Child Laborers.Data Requirements for Model-Based Cancer Prognosis PredictionFERAL: network-based classifier with application to breast cancer outcome prediction.De novo assembly, annotation, and characterization of the whole brain transcriptome of male and female Syrian hamstersThe Utility of Multiplex Assays for Identification of Proteomic Signatures in Psychiatry.Opportunities and Challenges of Multiplex Assays: A Machine Learning Perspective.Hierarchical classification of microorganisms based on high-dimensional phenotypic data.A 35-gene signature discriminates between rapidly- and slowly-progressing glioblastoma multiforme and predicts survival in known subtypes of the cancer.Applications of Bayesian network models in predicting types of hematological malignancies.
P2860
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P2860
Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.
description
2014 nî lūn-bûn
@nan
2014 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի հունիսին 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
Batch effect confounding leads ...... obtained by cross-validation.
@ast
Batch effect confounding leads ...... obtained by cross-validation.
@en
type
label
Batch effect confounding leads ...... obtained by cross-validation.
@ast
Batch effect confounding leads ...... obtained by cross-validation.
@en
prefLabel
Batch effect confounding leads ...... obtained by cross-validation.
@ast
Batch effect confounding leads ...... obtained by cross-validation.
@en
P2860
P1433
P1476
Batch effect confounding leads ...... obtained by cross-validation.
@en
P2093
Mauro Delorenzi
Sarah Gerster
P2860
P304
P356
10.1371/JOURNAL.PONE.0100335
P407
P577
2014-06-26T00:00:00Z