Deep learning identifies erroneous microarray-based, gene-level conclusions in literature
More than 110 000 publications have used microarrays to decipher phenotype-associated genes, clinical biomarkers and gene functions. Microarrays rely on digital assaying the fluorescence signals of arrays. In this study, we retrospectively constructed raw images for 37 724 published microarray data,...
Main Authors: | Chen, X. (Author), Guan, Y. (Author), Qin, Y. (Author), Yi, D. (Author) |
---|---|
Format: | Article |
Language: | English |
Published: |
Oxford University Press
2021
|
Subjects: | |
Online Access: | View Fulltext in Publisher |
Similar Items
-
Contaminated Chi-square Modeling and Its Application in Microarray Data Analysis
by: Zhou, Feng
Published: (2014) -
CuBlock: A cross-platform normalization method for gene-expression microarrays
by: Daura, X., et al.
Published: (2021) -
DNA microarrays: Recent Advances
by: Henry J. Herrera, et al.
Published: (2017-08-01) -
Development of a MIAME-compliant microarray data management system for functional genomics data integration
by: Oelofse, Andries Johannes
Published: (2013) -
Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE)
by: Koestler, D.C, et al.
Published: (2021)