ArticleNucleic acids research2015
Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.
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73 citing papers in PubMed.
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- HSTXGB: a hyperparameter self-tuning XGBoost method integrating pre- and post-processing for gene regulatory network inference.Frontiers in genetics · 2026Article
- Inferring causal trajectories from spatial transcriptomics using CASCAT.Nucleic acids research · 2025Article
- Quantifying direct associations between variables.Fundamental research · 2025Article
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- Determining interaction directionality in complex biochemical networks from stationary measurements.Scientific reports · 2025Article
- GTAT-GRN: a graph topology-aware attention method with multi-source feature fusion for gene regulatory network inference.Frontiers in genetics · 2025Article
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- ACOCMPMI: An Ant Colony Optimization Algorithm Based on Composite Multiscale Part Mutual Information for Detecting Epistatic Interactions.Human mutation · 2025Article
- NJGCG: A node-based joint Gaussian copula graphical model for gene networks inference across multiple states.Computational and structural biotechnology journal · 2024Article
- Reverse network diffusion to remove indirect noise for better inference of gene regulatory networks.Bioinformatics (Oxford, England) · 2024Article
- Inference of gene regulatory networks based on directed graph convolutional networks.Briefings in bioinformatics · 2024Article
- Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2024Article
- Inhibitory effects of Acanthopanax sessiliflorus Harms extract on the etiology of rheumatoid arthritis in a collagen-induced arthritis mouse model.Arthritis research & therapy · 2024Article
- CSER: a gene regulatory network construction method based on causal strength and ensemble regression.Frontiers in genetics · 2024Article
- Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.Bioinformatics and biology insights · 2024Review
- A mutual inclusion mechanism for precise boundary segmentation in medical images.Frontiers in bioengineering and biotechnology · 2024Article
- STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data.Bioinformatics (Oxford, England) · 2023Article
13 more citing papers are in PubMed but not listed here.
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Abstract
Mutual information (MI), a quantity describing the nonlinear dependence between two random variables, has been widely used to construct gene regulatory networks (GRNs). Despite its good performance, MI cannot separate the direct regulations from indirect ones among genes. Although the conditional mutual information (CMI) is able to identify the direct regulations, it generally underestimates the regulation strength, i.e. it may result in false negatives when inferring gene regulations. In this work, to overcome the problems, we propose a novel concept, namely conditional mutual inclusive information (CMI2), to describe the regulations between genes. Furthermore, with CMI2, we develop a new approach, namely CMI2NI (CMI2-based network inference), for reverse-engineering GRNs. In CMI2NI, CMI2 is used to quantify the mutual information between two genes given a third one through calculating the Kullback-Leibler divergence between the postulated distributions of including and excluding the edge between the two genes. The benchmark results on the GRNs from DREAM challenge as well as the SOS DNA repair network in Escherichia coli demonstrate the superior performance of CMI2NI. Specifically, even for gene expression data with small sample size, CMI2NI can not only infer the correct topology of the regulation networks but also accurately quantify the regulation strength between genes. As a case study, CMI2NI was also used to reconstruct cancer-specific GRNs using gene expression data from The Cancer Genome Atlas (TCGA). CMI2NI is freely accessible at http://www.comp-sysbio.org/cmi2ni.
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