ArticleBMC systems biology2019
GNE: a deep learning framework for gene network inference by aggregating biological information.
Article in BMC systems biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- IAGRN: An Interleaved-Attention Graph Neural Network for Gene Regulatory Network Inference.International journal of molecular sciences · 2026Article
- In Silico Perturbome Analysis Reveals Conserved Genes and Drug-Target Interactions inPathogens (Basel, Switzerland) · 2026Article
- A causal reinforcement learning framework for reliable gene regulatory network inference.BMC bioinformatics · 2026Article
- Inferring cell-specific gene regulatory networks based on causal graph embedding.Cell reports methods · 2026Article
- SGMHA: semantic graph reconstruction with multi-head attention for gene regulatory network inference.BMC genomics · 2026Article
- Conserved and divergent gene regulatory networks for crop drought resistance.Nature communications · 2026Article
- GRNFormer: accurate gene regulatory network inference using graph transformer.Bioinformatics (Oxford, England) · 2026Article
- CaHoT-GRN: context-aware high-order topology learning for robust single-cell gene regulatory network inference.Briefings in bioinformatics · 2026Article
- Revealing hidden regulatory dependencies: multi-perspective graph learning for single-cell gene regulatory network inference.Briefings in bioinformatics · 2026Article
- TDAGENE: Inference of Gene Regulatory Network Based on Topological Data Analysis and Graph Attention Network for Single-Cell RNA Sequencing Data.Computational and structural biotechnology journal · 2026Article
- Structure-enhanced graph meta learning for few-shot gene regulatory network inference.Genome biology · 2025Article
- GT-GRN: a graph transformer framework for enhanced gene regulatory network inference via multimodal embedding of expression data and existing network knowledge.Briefings in bioinformatics · 2025Article
- Core Perturbomes ofPathogens (Basel, Switzerland) · 2025Article
- Inferring cell-type-specific gene regulatory network from cellular transcriptomics data with GeneLink.Briefings in bioinformatics · 2025Article
- GRANet: a graph residual attention network for gene regulatory network inference.Briefings in bioinformatics · 2025Article
- Prediction of gene regulatory connections with joint single-cell foundation models and graph-based learning.Bioinformatics (Oxford, England) · 2025Article
- GAEDGRN: reconstruction of gene regulatory networks based on gravity-inspired graph autoencoders.Briefings in bioinformatics · 2025Article
- GRLGRN: graph representation-based learning to infer gene regulatory networks from single-cell RNA-seq data.BMC bioinformatics · 2025Article
- AnomalGRN: deciphering single-cell gene regulation network with graph anomaly detection.BMC biology · 2025Article
- GCLink: a graph contrastive link prediction framework for gene regulatory network inference.Bioinformatics (Oxford, England) · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundThe topological landscape of gene interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, it is still a challenging task to aggregate heterogeneous biological information such as gene expression and gene interactions to achieve more accurate inference for prediction and discovery of new gene interactions. In particular, how to generate a unified vector representation to integrate diverse input data is a key challenge addressed here.
resultsWe propose a scalable and robust deep learning framework to learn embedded representations to unify known gene interactions and gene expression for gene interaction predictions. These low- dimensional embeddings derive deeper insights into the structure of rapidly accumulating and diverse gene interaction networks and greatly simplify downstream modeling. We compare the predictive power of our deep embeddings to the strong baselines. The results suggest that our deep embeddings achieve significantly more accurate predictions. Moreover, a set of novel gene interaction predictions are validated by up-to-date literature-based database entries.
conclusionThe proposed model demonstrates the importance of integrating heterogeneous information about genes for gene network inference. GNE is freely available under the GNU General Public License and can be downloaded from GitHub ( https://github.com/kckishan/GNE ).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.