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- W2626778506 abstract "A major contributor to the scientific reproducibility crisis has been that the results from homogeneous, single-center studies do not generalize to heterogeneous, real world populations. Multi-cohort gene expression analysis has helped to increase reproducibility by aggregating data from diverse populations into a single analysis. To make the multi-cohort analysis process more feasible, we have assembled an analysis pipeline which implements rigorously studied meta-analysis best practices. We have compiled and made publicly available the results of our own multi-cohort gene expression analysis of 103 diseases, spanning 615 studies and 36,915 samples, through a novel and interactive web application. As a result, we have made both the process of and the results from multi-cohort gene expression analysis more approachable for non-technical users." @default.
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- W2626778506 date "2016-08-25" @default.
- W2626778506 modified "2023-10-13" @default.
- W2626778506 title "Empowering Multi-Cohort Gene Expression Analysis to Increase Reproducibility" @default.
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- W2626778506 doi "https://doi.org/10.1101/071514" @default.
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