Kernel machine approach to testing the significance of multiple genetic markers for risk prediction.
about
A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.Kernel methods for large-scale genomic data analysis.Global analysis of methylation profiles from high resolution CpG data.Insulin-like growth factor pathway genetic polymorphisms, circulating IGF1 and IGFBP3, and prostate cancer survival.GENE-LEVEL PHARMACOGENETIC ANALYSIS ON SURVIVAL OUTCOMES USING GENE-TRAIT SIMILARITY REGRESSION.Sequence kernel association test for survival traits.Incorporating auxiliary information for improved prediction using combination of kernel machinesAssessing gene-environment interactions for common and rare variants with binary traits using gene-trait similarity regressionKernel machine SNP-set analysis for censored survival outcomes in genome-wide association studies.Calcium intake, polymorphisms of the calcium-sensing receptor, and recurrent/aggressive prostate cancer.An Adaptive Genetic Association Test Using Double Kernel Machines.Region-based association tests for sequencing data on survival traits.Gene-Based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions.Kernel machine testing for risk prediction with stratified case cohort studies.Kernel machine SNP-set testing under multiple candidate kernels.Boosting the Power of the Sequence Kernel Association Test by Properly Estimating Its Null DistributionOmnibus risk assessment via accelerated failure time kernel machine modeling.MiRKAT-S: a community-level test of association between the microbiota and survival times.Identifying predictive markers for personalized treatment selection.FLCRM: Functional linear cox regression model.Integrative genomic testing of cancer survival using semiparametric linear transformation models.SNP Set Association Testing for Survival Outcomes in the Presence of Intrafamilial Correlation.Kernel machine score test for pathway analysis in the presence of semi-competing risks.Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures.Conditional asymptotic inference for the kernel association test.Pathway aggregation for survival prediction via multiple kernel learning.Insulin-like Growth Factor Pathway Genetic Polymorphisms, Circulating IGF1 and IGFBP3, and Prostate Cancer Survival.
P2860
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P2860
Kernel machine approach to testing the significance of multiple genetic markers for risk prediction.
description
2011 nî lūn-bûn
@nan
2011 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի հունվարին 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
Kernel machine approach to tes ...... c markers for risk prediction.
@ast
Kernel machine approach to tes ...... c markers for risk prediction.
@en
type
label
Kernel machine approach to tes ...... c markers for risk prediction.
@ast
Kernel machine approach to tes ...... c markers for risk prediction.
@en
prefLabel
Kernel machine approach to tes ...... c markers for risk prediction.
@ast
Kernel machine approach to tes ...... c markers for risk prediction.
@en
P2093
P2860
P1433
P1476
Kernel machine approach to tes ...... c markers for risk prediction.
@en
P2093
Giulia Tonini
Tianxi Cai
Xihong Lin
P2860
P304
P356
10.1111/J.1541-0420.2010.01544.X
P407
P577
2011-01-31T00:00:00Z