ArticlePLoS computational biology2019
Predicting gene regulatory interactions based on spatial gene expression data and deep learning.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed, 60 citations in OpenAlex.
- GRNFormer: accurate gene regulatory network inference using graph transformer.Bioinformatics (Oxford, England) · 2026Article
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- Refining computational inference of gene regulatory networks: integrating knockout data within a multi-task framework.Briefings in bioinformatics · 2024Article
- Inference of Gene Regulatory Networks Based on Multi-view Hierarchical Hypergraphs.Interdisciplinary sciences, computational life sciences · 2024Article
- scGREAT: Transformer-based deep-language model for gene regulatory network inference from single-cell transcriptomics.iScience · 2024Article
- Automatic design of gene regulatory mechanisms for spatial pattern formation.NPJ systems biology and applications · 2024Article
- MIGGRI: A multi-instance graph neural network model for inferring gene regulatory networks for Drosophila from spatial expression images.PLoS computational biology · 2023Article
- iLSGRN: inference of large-scale gene regulatory networks based on multi-model fusion.Bioinformatics (Oxford, England) · 2023Article
- Knowledge graph embedding for profiling the interaction between transcription factors and their target genes.PLoS computational biology · 2023Article
- Subcellular spatially resolved gene neighborhood networks in single cells.Cell reports methods · 2023Article
- An approach of gene regulatory network construction using mixed entropy optimizing context-related likelihood mutual information.Bioinformatics (Oxford, England) · 2023Article
- Cell-type modeling in spatial transcriptomics data elucidates spatially variable colocalization and communication between cell-types in mouse brain.Cell systems · 2022Article
- Prediction of Time Series Gene Expression and Structural Analysis of Gene Regulatory Networks Using Recurrent Neural Networks.Entropy (Basel, Switzerland) · 2022Article
- OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data.PLoS computational biology · 2022Article
- Deep learning trained on hematoxylin and eosin tumor region of Interest predicts HER2 status and trastuzumab treatment response in HER2+ breast cancer.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Article
- Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science.Entropy (Basel, Switzerland) · 2021Review
- Biologically relevant transfer learning improves transcription factor binding prediction.Genome biology · 2021Article
- SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions.BMC bioinformatics · 2021Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reverse engineering of gene regulatory networks (GRNs) is a central task in systems biology. Most of the existing methods for GRN inference rely on gene co-expression analysis or TF-target binding information, where the determination of co-expression is often unreliable merely based on gene expression levels, and the TF-target binding data from high-throughput experiments may be noisy, leading to a high ratio of false links and missed links, especially for large-scale networks. In recent years, the microscopy images recording spatial gene expression have become a new resource in GRN reconstruction, as the spatial and temporal expression patterns contain much abundant gene interaction information. Till now, the spatial expression resources have been largely underexploited, and only a few traditional image processing methods have been employed in the image-based GRN reconstruction. Moreover, co-expression analysis using conventional measurements based on image similarity may be inaccurate, because it is the local-pattern consistency rather than global-image-similarity that determines gene-gene interactions. Here we present GripDL (Gene regulatory interaction prediction via Deep Learning), which incorporates high-confidence TF-gene regulation knowledge from previous studies, and constructs GRNs for Drosophila eye development based on Drosophila embryonic gene expression images. Benefitting from the powerful representation ability of deep neural networks and the supervision information of known interactions, the new method outperforms traditional methods with a large margin and reveals new intriguing knowledge about Drosophila eye development.
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