ArticleBioinformatics (Oxford, England)2015
Combining tree-based and dynamical systems for the inference of gene regulatory networks.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
- From time-course expression to gene regulation: direct linear ODE inference without finite-difference approximation.bioRxiv : the preprint server for biology · 2026Article
- HSTXGB: a hyperparameter self-tuning XGBoost method integrating pre- and post-processing for gene regulatory network inference.Frontiers in genetics · 2026Article
- Double optimal transport for differential gene regulatory network inference with unpaired samples.Bioinformatics (Oxford, England) · 2025Article
- Reconstructing Dynamic Gene Regulatory Networks Using f-Divergence from Time-Series scRNA-Seq Data.Current issues in molecular biology · 2025Article
- scMINER: a mutual information-based framework for clustering and hidden driver inference from single-cell transcriptomics data.Nature communications · 2025Article
- Extreme events in gene regulatory networks with time-delays.Scientific reports · 2025Article
- Optimized network inference for immune diseased single cells.Frontiers in immunology · 2025Article
- 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
- Inference of gene regulatory networks based on directed graph convolutional networks.Briefings in bioinformatics · 2024Article
- DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding.Briefings in bioinformatics · 2024Article
- Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.Bioinformatics and biology insights · 2024Review
- Review
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- Studying stochastic systems biology of the cell with single-cell genomics data.bioRxiv : the preprint server for biology · 2023Article
- Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data.NAR genomics and bioinformatics · 2023Article
- Network inference with Granger causality ensembles on single-cell transcriptomics.Cell reports · 2022Article
- Inferring causal gene regulatory networkFrontiers in bioengineering and biotechnology · 2022Article
- Breaking the Immune Complexity of the Tumor Microenvironment Using Single-Cell Technologies.Frontiers in genetics · 2022Review
- Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline.Journal of visualized experiments : JoVE · 2021Article
- From bench to bedside: Single-cell analysis for cancer immunotherapy.Cancer cell · 2021Review
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationReconstructing the topology of gene regulatory networks (GRNs) from time series of gene expression data remains an important open problem in computational systems biology. Existing GRN inference algorithms face one of two limitations: model-free methods are scalable but suffer from a lack of interpretability and cannot in general be used for out of sample predictions. On the other hand, model-based methods focus on identifying a dynamical model of the system. These are clearly interpretable and can be used for predictions; however, they rely on strong assumptions and are typically very demanding computationally.
resultsHere, we propose a new hybrid approach for GRN inference, called Jump3, exploiting time series of expression data. Jump3 is based on a formal on/off model of gene expression but uses a non-parametric procedure based on decision trees (called 'jump trees') to reconstruct the GRN topology, allowing the inference of networks of hundreds of genes. We show the good performance of Jump3 on in silico and synthetic networks and applied the approach to identify regulatory interactions activated in the presence of interferon gamma.
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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.