Application of multi-SNP approaches Bayesian LASSO and AUC-RF to detect main effects of inflammatory-gene variants associated with bladder cancer risk.
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Modeling X Chromosome Data Using Random Forests: Conquering Sex BiasToward the integration of Omics data in epidemiological studies: still a "long and winding road".Sparse regressions for predicting and interpreting subcellular localization of multi-label proteinsA fast algorithm for Bayesian multi-locus model in genome-wide association studies.Impact of smoking status and cumulative smoking exposure on tumor recurrence of non-muscle-invasive bladder cancer.Next generation modeling in GWAS: comparing different genetic architectures.
P2860
Application of multi-SNP approaches Bayesian LASSO and AUC-RF to detect main effects of inflammatory-gene variants associated with bladder cancer risk.
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2013 nî lūn-bûn
@nan
2013 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2013 թվականի դեկտեմբերին հրատարակված գիտական հոդված
@hy
2013年の論文
@ja
2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
@wuu
name
Application of multi-SNP appro ...... ated with bladder cancer risk.
@ast
Application of multi-SNP appro ...... ated with bladder cancer risk.
@en
type
label
Application of multi-SNP appro ...... ated with bladder cancer risk.
@ast
Application of multi-SNP appro ...... ated with bladder cancer risk.
@en
prefLabel
Application of multi-SNP appro ...... ated with bladder cancer risk.
@ast
Application of multi-SNP appro ...... ated with bladder cancer risk.
@en
P2093
P2860
P50
P1433
P1476
Application of multi-SNP appro ...... ated with bladder cancer risk.
@en
P2093
Alfredo Carrato
Arcadi Navarro
Debra T Silverman
Gemma Vellalta
M Luz Calle
Núria Malats
Sandra Petrus
Víctor Urrea
Yuanqing Ye
P2860
P304
P356
10.1371/JOURNAL.PONE.0083745
P407
P50
P577
2013-12-31T00:00:00Z