Deep learning identifies erroneous microarray-based, gene-level conclusions in literature.

ABSTRACT: 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, and developed deep learning algorithms to automatically detect systematic defects. We report that an alarming amount of 26.73% of the microarray-based studies are affected by serious imaging defects. By literature mining, we found that publications associated with these affected microarrays have reported disproportionately more biological discoveries on the genes in the contaminated areas compared to other genes. 28.82% of the gene-level conclusions reported in these publications were based on

SUBMITTER: Qin Y 

PROVIDER: S-EPMC8489595 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature