ArticleNature biotechnology2013
Network deconvolution as a general method to distinguish direct dependencies in networks.
Article in Nature biotechnology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 109 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
109 citing papers in PubMed.
- Regional environmental heterogeneity under contrasting anthropogenic pressures has differential effects on particle-attached than free-living bacteria communities in coral reef waters.Microbiology spectrum · 2026Article
- MR2G: A novel framework for causal network inference using GWAS summary data.PLoS genetics · 2026Article
- Methods for Prioritizing Causal Genes in Molecular Studies of Human Disease: The State of the Art.Genetic epidemiology · 2026Review
- HONeD-in on Brain Activity: Deconvolving Passive Diffusion on the Structural Network from Functional Brain Signals.bioRxiv : the preprint server for biology · 2026Article
- Leveraging network motifs to improve artificial neural networks.Nature communications · 2025Article
- Combining Motifs, CRE Activity, And Gene Expression Data Using ML Greatly Improves the Accuracy of Tissue-Specific TF Network Maps.bioRxiv : the preprint server for biology · 2025Article
- TRENDY: gene regulatory network inference enhanced by transformer.Bioinformatics (Oxford, England) · 2025Article
- Bayesian causal graphical model for joint Mendelian randomization analysis of multiple exposures and outcomes.American journal of human genetics · 2025Article
- On network deconvolution for undirected graphs.Biometrics · 2024Article
- Estimation of a genetic Gaussian network using GWAS summary data.Biometrics · 2024Article
- MARML: Motif-Aware Deep Representation Learning in Multilayer Networks.IEEE transactions on neural networks and learning systems · 2024Article
- Unraveling emergent network indeterminacy in complex ecosystems: A random matrix approach.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Reverse network diffusion to remove indirect noise for better inference of gene regulatory networks.Bioinformatics (Oxford, England) · 2024Article
- MICA: a multi-omics method to predict gene regulatory networks in early human embryos.Life science alliance · 2024Article
- Shotgun metagenomics reveals interkingdom association between intestinal bacteria and fungi involving competition for nutrients.Microbiome · 2023Article
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- Article
- Widening the landscape of transcriptional regulation of green algal photoprotection.Nature communications · 2023Article
- Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data.PLoS genetics · 2023Article
- Structure learning for gene regulatory networks.PLoS computational biology · 2023Article
49 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on byNetwork cleanup.2013
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
Recognizing direct relationships between variables connected in a network is a pervasive problem in biological, social and information sciences as correlation-based networks contain numerous indirect relationships. Here we present a general method for inferring direct effects from an observed correlation matrix containing both direct and indirect effects. We formulate the problem as the inverse of network convolution, and introduce an algorithm that removes the combined effect of all indirect paths of arbitrary length in a closed-form solution by exploiting eigen-decomposition and infinite-series sums. We demonstrate the effectiveness of our approach in several network applications: distinguishing direct targets in gene expression regulatory networks; recognizing directly interacting amino-acid residues for protein structure prediction from sequence alignments; and distinguishing strong collaborations in co-authorship social networks using connectivity information alone. In addition to its theoretical impact as a foundational graph theoretic tool, our results suggest network deconvolution is widely applicable for computing direct dependencies in network science across diverse disciplines.
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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.