ArticleProceedings of the National Academy of Sciences of the United States of America2016
Part mutual information for quantifying direct associations in networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers.
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88 citing papers in PubMed.
- From prior knowledge to data-informed models: a review of Boolean network inference.Briefings in bioinformatics · 2026Review
- Pathway potency in methylation altered network reveals lung cancer branching evolution and hallmarks.iScience · 2026Article
- BCTI: a Bayesian network-based method for revealing critical transitions in complex biological systems.PeerJ · 2026Article
- Mining higher-order triadic interactions.Nature communications · 2025Article
- Detecting dynamical causality by intersection cardinal concavity.Fundamental research · 2025Article
- Quantifying interventional causality by knockoff operation.Science advances · 2025Article
- Gene Regulatory Network Inference from Pseudotime-Ordered scRNA-seq Data via Time-Lagged Divergence Measures.Bioinformatics research and applications : ... international symposium, ISBRA ... proceedings. ISBRA (Conference) · 2025Article
- Double optimal transport for differential gene regulatory network inference with unpaired samples.Bioinformatics (Oxford, England) · 2025Article
- PGBTR: a powerful and general method for inferring bacterial transcriptional regulatory networks.BMC genomics · 2025Article
- Quantifying direct associations between variables.Fundamental research · 2025Article
- AJGM: joint learning of heterogeneous gene networks with adaptive graphical model.Bioinformatics (Oxford, England) · 2025Article
- Review
- ACOCMPMI: An Ant Colony Optimization Algorithm Based on Composite Multiscale Part Mutual Information for Detecting Epistatic Interactions.Human mutation · 2025Article
- WCSGNet: a graph neural network approach using weighted cell-specific networks for cell-type annotation in scRNA-seq.Frontiers in genetics · 2025Article
- GTAT-GRN: a graph topology-aware attention method with multi-source feature fusion for gene regulatory network inference.Frontiers in genetics · 2025Article
- Informeasure: an R/bioconductor package for quantifying nonlinear dependence between variables in biological networks from an information theory perspective.BMC bioinformatics · 2024Article
- NJGCG: A node-based joint Gaussian copula graphical model for gene networks inference across multiple states.Computational and structural biotechnology journal · 2024Article
- Unraveling Spatially Diverse and Interactive Regulatory Mechanisms of Wetland Methane Fluxes to Improve Emission Estimation.Environmental science & technology · 2024Article
- Reverse network diffusion to remove indirect noise for better inference of gene regulatory networks.Bioinformatics (Oxford, England) · 2024Article
- Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.Bioinformatics and biology insights · 2024Review
28 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
Quantitatively identifying direct dependencies between variables is an important task in data analysis, in particular for reconstructing various types of networks and causal relations in science and engineering. One of the most widely used criteria is partial correlation, but it can only measure linearly direct association and miss nonlinear associations. However, based on conditional independence, conditional mutual information (CMI) is able to quantify nonlinearly direct relationships among variables from the observed data, superior to linear measures, but suffers from a serious problem of underestimation, in particular for those variables with tight associations in a network, which severely limits its applications. In this work, we propose a new concept, "partial independence," with a new measure, "part mutual information" (PMI), which not only can overcome the problem of CMI but also retains the quantification properties of both mutual information (MI) and CMI. Specifically, we first defined PMI to measure nonlinearly direct dependencies between variables and then derived its relations with MI and CMI. Finally, we used a number of simulated data as benchmark examples to numerically demonstrate PMI features and further real gene expression data from Escherichia coli and yeast to reconstruct gene regulatory networks, which all validated the advantages of PMI for accurately quantifying nonlinearly direct associations in networks.
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