ArticleProceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence2021
Gene regulatory network inference as relaxed graph matching.
Article in Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Large-scale, interpretable gene regulatory network inference through biologically informed matrix factorization.bioRxiv : the preprint server for biology · 2026Article
- SGMHA: semantic graph reconstruction with multi-head attention for gene regulatory network inference.BMC genomics · 2026Article
- Reconstructing Dynamic Gene Regulatory Networks Using f-Divergence from Time-Series scRNA-Seq Data.Current issues in molecular biology · 2025Article
- HGATLink: single-cell gene regulatory network inference via the fusion of heterogeneous graph attention networks and transformer.BMC bioinformatics · 2025Article
- Reproducible processing of TCGA regulatory networks.GigaScience · 2025Article
- Predicting TF-Target Gene Association Using a Heterogeneous Network and Enhanced Negative Sampling.Bioinformatics and biology insights · 2025Article
- Bayesian inference of sample-specific coexpression networks.Genome research · 2024Article
- Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.BioEssays : news and reviews in molecular, cellular and developmental biology · 2024Review
- Biologically informed NeuralODEs for genome-wide regulatory dynamics.Genome biology · 2024Article
- Network-based drug repurposing for schizophrenia.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Article
- Gene regulatory network inference based on causal discovery integrating with graph neural network.Quantitative biology (Beijing, China) · 2023Article
- Bayesian Optimized sample-specific Networks Obtained By Omics data (BONOBO).bioRxiv : the preprint server for biology · 2023Article
- Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions.Yearbook of medical informatics · 2023Review
- Knowledge graph embedding for profiling the interaction between transcription factors and their target genes.PLoS computational biology · 2023Article
- Biologically informed NeuralODEs for genome-wide regulatory dynamics.Research square · 2023Article
- The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks.Genome biology · 2023Article
- GRAND: a database of gene regulatory network models across human conditions.Nucleic acids research · 2022Article
- Cascade Size Distributions: Why They Matter and How to Compute Them Efficiently.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Bipartite network inference is a ubiquitous problem across disciplines. One important example in the field molecular biology is gene regulatory network inference. Gene regulatory networks are an instrumental tool aiding in the discovery of the molecular mechanisms driving diverse diseases, including cancer. However, only noisy observations of the projections of these regulatory networks are typically assayed. In an effort to better estimate regulatory networks from their noisy projections, we formulate a non-convex but analytically tractable optimization problem called OTTER. This problem can be interpreted as relaxed graph matching between the two projections of the bipartite network. OTTER's solutions can be derived explicitly and inspire a spectral algorithm, for which we provide network recovery guarantees. We also provide an alternative approach based on gradient descent that is more robust to noise compared to the spectral algorithm. Interestingly, this gradient descent approach resembles the message passing equations of an established gene regulatory network inference method, PANDA. Using three cancer-related data sets, we show that OTTER outperforms state-of-the-art inference methods in predicting transcription factor binding to gene regulatory regions. To encourage new graph matching applications to this problem, we have made all networks and validation data publicly available.
Identifiers
34707916PMC8546743What OpenQuestion holds
Registered trials
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.