Microarray Quality Control Consortium Phase II
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2010
- The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
( Nat Biotechnol, 28: 827-838, 2010 )
Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent ...
- Consistency of predictive signature genes and classifiers generated using different microarray platforms
( The Pharmacogenomics Journal (2010) 10, 247–257 )
Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of the classifiers and signature genes in cross-platform prediction applications remains ...
- Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes
( The Pharmacogenomics Journal (2010) 10, 310–323 )
Gene expression signatures of toxicity and clinical response benefit both safety assessment and clinical practice; however, difficulties in connecting signature genes with the predicted end points have limited their application. ...
- Genomic indicators in the blood predict drug-induced liver injury
( The Pharmacogenomics Journal (2010) 10, 267–277 )
Genomic biomarkers for the detection of drug-induced liver injury (DILI) from blood are urgently needed for monitoring drug safety. We used a unique data set as part of the Food ...
- A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data
( The Pharmacogenomics Journal (2010) 10, 278–291 )
Batch effects are the systematic non-biological differences between batches (groups) of samples in microarray experiments due to various causes such as differences in sample preparation and hybridization protocols. Previous work ...
- Effect of training-sample size and classification difficulty on the accuracy of genomic predictors
( Breast Cancer Research 2010, 12:R5 )
Mary E. Edgerton, a pathologist performing cancer research at MD Anderson and a long-term client, says “I have been using Metacore for many years in my research into pathways and networks that control aggressive behavior in cancers. I am currently using it to infer networks from analysis of gene expression array data for pathways in lung, brain, and breast cancer. I also use the curated pathways to formulate mathematical models of molecular networks that predict tumor behavior using multiscale modeling. Not only do I find the product to be very useful, but I also appreciate the responsiveness of the staff at Genego to my technical questions and suggestions.”