MIGGRI: A multi-instance graph neural network model for inferring gene regulatory networks for Drosophila from spatial expression images.

ABSTRACT: Recent breakthrough in spatial transcriptomics has brought great opportunities for exploring gene regulatory networks (GRNs) from a brand-new perspective. Especially, the local expression patterns and spatio-temporal regulation mechanisms captured by spatial expression images allow more delicate delineation of the interplay between transcript factors and their target genes. However, the complexity and size of spatial image collections pose significant challenges to GRN inference using image-based methods. Extracting regulatory information from expression images is difficult due to the lack of supervision and the multi-instance nature of the problem, where a gene often corresponds to multiple images captured from different views. While graph models, particularly graph neural networks, have

SUBMITTER: Huang Y 

PROVIDER: S-EPMC10659162 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature