Erratum: Microarray data analysis: from disarray to consolidation and consensus
TL;DR: The y- axis label for Figure 1d was incorrect and the correct y-axis label should be .
Abstract: Nature Reviews Genetics 7 55-65 (2006) In this article, the y-axis label for Figure 1d was incorrect. The correct y-axis label should be . The authors apologize for the error.
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TL;DR: P predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans are generated.
Abstract: 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 teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.
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TL;DR: This Review discusses relevant concepts, computational methods and software tools for analysing and interpreting DNA methylation data, and focuses not only on the bioinformatic challenges of large epigenome-mapping projects and methylation-wide association studies but also highlights software tools that make genome-wide DNAmethylation mapping more accessible for laboratories with limited bioinformatics experience.
Abstract: DNA methylation is an epigenetic mark that has suspected regulatory roles in a broad range of biological processes and diseases. The technology is now available for studying DNA methylation genome-wide, at a high resolution and in a large number of samples. This Review discusses relevant concepts, computational methods and software tools for analysing and interpreting DNA methylation data. It focuses not only on the bioinformatic challenges of large epigenome-mapping projects and epigenome-wide association studies but also highlights software tools that make genome-wide DNA methylation mapping more accessible for laboratories with limited bioinformatics experience.
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...This is because results from random experimental measurements would favor the null hypothesis that gene expressions are the same between sample groups [40]....
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TL;DR: This Review discusses how high-throughput molecular, integrative and network approaches inform disease biology by placing human genetics in a molecular systems and neurobiological context and provides a framework for interpreting network biology studies and leveraging big genomics data sets in neurobiology.
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