ArticlePloS one2016
Nonlinear Network Reconstruction from Gene Expression Data Using Marginal Dependencies Measured by DCOL.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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.
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Who cites it
12 citing papers in PubMed.
- MulNet: a scalable framework for reconstructing intra- and intercellular signaling networks from bulk and single-cell RNA-seq data.Briefings in bioinformatics · 2025Article
- Inferring gene regulatory networks from time-series scRNA-seq data via GRANGER causal recurrent autoencoders.Briefings in bioinformatics · 2025Article
- Cell Fate Dynamics Reconstruction Identifies TPT1 and PTPRZ1 Feedback Loops as Master Regulators of Differentiation in Pediatric Glioblastoma-Immune Cell Networks.Interdisciplinary sciences, computational life sciences · 2025Article
- CVGAE: A Self-Supervised Generative Method for Gene Regulatory Network Inference Using Single-Cell RNA Sequencing Data.Interdisciplinary sciences, computational life sciences · 2024Article
- Studying temporal dynamics of single cells: expression, lineage and regulatory networks.Biophysical reviews · 2024Review
- Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.Bioinformatics and biology insights · 2024Review
- Article
- Gene Co-Expression Network Tools and Databases for Crop Improvement.Plants (Basel, Switzerland) · 2022Review
- A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data.Briefings in bioinformatics · 2021Review
- Differential Network Analysis Reveals Regulatory Patterns in Neural Stem Cell Fate Decision.Interdisciplinary sciences, computational life sciences · 2021Article
- Estimating Linear and Nonlinear Gene Coexpression Networks by Semiparametric Neighborhood Selection.Genetics · 2020Article
- Mapping gene regulatory networks from single-cell omics data.Briefings in functional genomics · 2018Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Reconstruction of networks from high-throughput expression data is an important tool to identify new regulatory relations. Given that nonlinear and complex relations exist between biological units, methods that can utilize nonlinear dependencies may yield insights that are not provided by methods using linear associations alone. We have previously developed a distance to measure predictive nonlinear relations, the Distance based on Conditional Ordered List (DCOL), which is sensitive and computationally efficient on large matrices. In this study, we explore the utility of DCOL in the reconstruction of networks, by combining it with local false discovery rate (lfdr)-based inference. We demonstrate in simulations that the new method named nlnet is effective in recovering hidden nonlinear modules. We also demonstrate its utility using a single cell RNA seq dataset. The method is available as an R package at https://cran.r-project.org/web/packages/nlnet.
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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.