Evaluation of deep convolutional neural networks for in situ hybridization gene expression image representation.

ABSTRACT: High resolution in situ hybridization (ISH) images of the brain capture spatial gene expression at cellular resolution. These spatial profiles are key to understanding brain organization at the molecular level. Previously, manual qualitative scoring and informatics pipelines have been applied to ISH images to determine expression intensity and pattern. To better capture the complex patterns of gene expression in the human cerebral cortex, we applied a machine learning approach. We propose gene re-identification as a contrastive learning task to compute representations of ISH images. We train our model on an ISH dataset of ~1,000 genes obtained from postmortem samples from 42 individuals. This model reaches a gene re-identification rate of 38.3%, a 13x improvement over random chance. We fin

SUBMITTER: Abed-Esfahani P 

PROVIDER: S-EPMC8786163 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature