ArticleBMC bioinformatics2006
Discovery of Time-Delayed Gene Regulatory Networks based on temporal gene expression profiling.
Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Dissecting reversible and irreversible single cell state transitions from gene regulatory networks.Molecular systems biology · 2026Article
- Reconstruction of gene regulatory networks for Caenorhabditis elegans using tree-shaped gene expression data.Briefings in bioinformatics · 2024Article
- Inference of Large-scale Time-delayed Gene Regulatory Network with Parallel MapReduce Cloud Platform.Scientific reports · 2018Article
- The G-Box Transcriptional Regulatory Code in Arabidopsis.Plant physiology · 2017Article
- Mono- and sesquiterpene release from tomato (Environmental and experimental botany · 2016Article
- Inferring Broad Regulatory Biology from Time Course Data: Have We Reached an Upper Bound under Constraints Typical of In Vivo Studies?PloS one · 2015Article
- Learning delayed influences of biological systems.Frontiers in bioengineering and biotechnology · 2014Article
- Gene regulatory network discovery using pairwise Granger causality.IET systems biology · 2013Article
- Discovering time-lagged rules from microarray data using gene profile classifiers.BMC bioinformatics · 2011Article
- High dimensional data analysis using multivariate generalized spatial quantiles.Journal of multivariate analysis · 2011Article
- Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates.BMC systems biology · 2011Article
- TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach.BMC bioinformatics · 2010Article
- Inferring Transcriptional Interactions by the Optimal Integration of ChIP-chip and Knock-out Data.Bioinformatics and biology insights · 2009Article
- Estimating equation-based causality analysis with application to microarray time series data.Biostatistics (Oxford, England) · 2009Article
- Grouped graphical Granger modeling for gene expression regulatory networks discovery.Bioinformatics (Oxford, England) · 2009Article
- Constructing disease-specific gene networks using pair-wise relevance metric: application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements.BMC systems biology · 2008Article
- Modeling human cancer-related regulatory modules by GA-RNN hybrid algorithms.BMC bioinformatics · 2007Article
- Single-nucleotide polymorphism-gene intermixed networking reveals co-linkers connected to multiple gene expression phenotypes.BMC proceedings · 2007Article
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Authors and funding
13 authors.
Funding
Abstract
backgroundIt is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks.
resultsIn particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network) to address the underlying regulations of genes that can span any unit(s) of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings.
conclusionWe established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex gene regulations related to the development, aging and progressive pathogenesis of a complex disease where potential dependences between different experiment units might occurs.
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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.