The US Food and Drug Administration MicroArray Quality Control (MAQC) Project - Phase II


Volume 10, Issue 4 (August 2010)



Of genomics and bioinformatics FREE

W Slikker Jr

Pharmacogenomics J 10: 245-246; doi:10.1038/tpj.2010.59


Original Articles

Consistency of predictive signature genes and classifiers generated using different microarray platforms Open

X Fan, E K Lobenhofer, M Chen, W Shi, J Huang, J Luo, J Zhang, S J Walker, T-M Chu, L Li, R Wolfinger, W Bao, R S Paules, P R Bushel, J Li, T Shi, T Nikolskaya, Y Nikolsky, H Hong, Y Deng, Y Cheng, H Fang, L Shi and W Tong

Pharmacogenomics J 10: 247-257; doi:10.1038/tpj.2010.34

Comparison of performance of one-color and two-color gene-expression analyses in predicting clinical endpoints of neuroblastoma patients Open

A Oberthuer, D Juraeva, L Li, Y Kahlert, F Westermann, R Eils, F Berthold, L Shi, R D Wolfinger, M Fischer and B Brors

Pharmacogenomics J 10: 258-266; doi:10.1038/tpj.2010.53

Genomic indicators in the blood predict drug-induced liver injury FREE

J Huang, W Shi, J Zhang, J W Chou, R S Paules, K Gerrish, J Li, J Luo, R D Wolfinger, W Bao, T-M Chu, Y Nikolsky, T Nikolskaya, D Dosymbekov, M O Tsyganova, L Shi, X Fan, J C Corton, M Chen, Y Cheng, W Tong, H Fang and P R Bushel

Pharmacogenomics J 10: 267-277; doi:10.1038/tpj.2010.33

A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data Open

J Luo, M Schumacher, A Scherer, D Sanoudou, D Megherbi, T Davison, T Shi, W Tong, L Shi, H Hong, C Zhao, F Elloumi, W Shi, R Thomas, S Lin, G Tillinghast, G Liu, Y Zhou, D Herman, Y Li, Y Deng, H Fang, P Bushel, M Woods and J Zhang

Pharmacogenomics J 10: 278-291; doi:10.1038/tpj.2010.57

k-Nearest neighbor models for microarray gene expression analysis and clinical outcome prediction Open

R M Parry, W Jones, T H Stokes, J H Phan, R A Moffitt, H Fang, L Shi, A Oberthuer, M Fischer, W Tong and M D Wang

Pharmacogenomics J 10: 292-309; doi:10.1038/tpj.2010.56

Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes Open

W Shi, M Bessarabova, D Dosymbekov, Z Dezso, T Nikolskaya, M Dudoladova, T Serebryiskaya, A Bugrim, A Guryanov, R J Brennan, R Shah, J Dopazo, M Chen, Y Deng, T Shi, G Jurman, C Furlanello, R S Thomas, J C Corton, W Tong, L Shi and Y Nikolsky

Pharmacogenomics J 10: 310-323; doi:10.1038/tpj.2010.35

Variability in GWAS analysis: the impact of genotype calling algorithm inconsistencies FREE

K Miclaus, M Chierici, C Lambert, L Zhang, S Vega, H Hong, S Yin, C Furlanello, R Wolfinger and F Goodsaid

Pharmacogenomics J 10: 324-335; doi:10.1038/tpj.2010.46

Batch effects in the BRLMM genotype calling algorithm influence GWAS results for the Affymetrix 500K array FREE

K Miclaus, R Wolfinger, S Vega, M Chierici, C Furlanello, C Lambert, H Hong, Li Zhang, S Yin and F Goodsaid

Pharmacogenomics J 10: 336-346; doi:10.1038/tpj.2010.36

Assessment of variability in GWAS with CRLMM genotyping algorithm on WTCCC coronary artery disease FREE

L Zhang, S Yin, K Miclaus, M Chierici, S Vega, C Lambert, H Hong, R D Wolfinger, C Furlanello and F Goodsaid

Pharmacogenomics J 10: 347-354; doi:10.1038/tpj.2010.27

An interactive effect of batch size and composition contributes to discordant results in GWAS with the CHIAMO genotyping algorithm FREE

M Chierici, K Miclaus, S Vega and C Furlanello

Pharmacogenomics J 10: 355-363; doi:10.1038/tpj.2010.47

Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples Open

H Hong, L Shi, Z Su, W Ge, W D Jones, W Czika, K Miclaus, C G Lambert, S C Vega, J Zhang, B Ning, J Liu, B Green, L Xu, H Fang, R Perkins, S M Lin, N Jafari, K Park, T Ahn, M Chierici, C Furlanello, L Zhang, R D Wolfinger, F Goodsaid and W Tong

Pharmacogenomics J 10: 364-374; advance online publication, April 6, 2010; doi:10.1038/tpj.2010.24