ReviewMethods in molecular biology (Clifton, N.J.)2010
Computational methods for analyzing dynamic regulatory networks.
Review in Methods in molecular biology (Clifton, N.J.), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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
4 citing papers in PubMed.
- Regulatory network inferred using expression data of small sample size: application and validation in erythroid system.Bioinformatics (Oxford, England) · 2015Article
- Parameter-less approaches for interpreting dynamic cellular response.Journal of biological engineering · 2014Article
- Linking the signaling cascades and dynamic regulatory networks controlling stress responses.Genome research · 2013Article
- Reconstruction of regulatory networks through temporal enrichment profiling and its application to H1N1 influenza viral infection.BMC bioinformatics · 2013Article
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Authors and funding
3 authors.
Funding
Abstract
Regulatory and other networks in the cell change in a highly dynamic way over time and in response to internal and external stimuli. While several different types of high-throughput experimental procedures are available to study systems in the cell, most only measure static properties of such networks. Information derived from sequence data is inherently static, and most interaction data sets are measured in a static way as well. In this chapter we discuss one of the few abundant sources for temporal information, time series expression data. We provide an overview of the methods suggested for clustering this type of data to identify functionally related genes. We also discuss methods for inferring causality and interactions using lagged correlations and regression analysis. Finally, we present methods for combining time series expression data with static data to reconstruct dynamic regulatory networks. We point to software tools implementing the methods discussed in this chapter. As more temporal measurements become available, the importance of analyzing such data and of combining it with other types of data will greatly increase.
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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.