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Related Concept Videos

Survival Tree01:19

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Related Experiment Video

Updated: Jun 8, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Inferring gene regression networks with model trees.

Isabel A Nepomuceno-Chamorro1, Jesus S Aguilar-Ruiz, Jose C Riquelme

  • 1Dpt Lenguajes y Sistemas Informaticos, Universidad de Sevilla, Seville, Spain. inepomuceno@us.es

BMC Bioinformatics
|October 19, 2010
PubMed
Summary

We introduce REGNET, a novel method using model trees to build gene regulatory networks from gene expression data. REGNET accurately identifies gene interactions, outperforming traditional correlation-based approaches by detecting local similarities.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Microarray technologies generate vast amounts of data, necessitating advanced strategies for analysis.
  • Inferring gene regulatory networks is crucial for understanding biological systems, with gene co-expression networks as a key first step.
  • Current correlation-based methods for gene co-expression networks excel at detecting global similarities but often miss local gene interactions.

Purpose of the Study:

  • To develop a novel computational method for inferring gene interaction networks from gene expression data.
  • To address the limitations of correlation-based methods in detecting local gene similarities.
  • To provide a more accurate approach for constructing gene association networks.

Main Methods:

  • Propose REGNET, a method utilizing model trees to construct gene interaction networks.
  • For each gene, a single regression tree is generated using the remaining genes as predictors.
  • A statistical procedure to control the false discovery rate is applied to build the final gene association network graph.

Main Results:

  • REGNET was experimentally validated on Saccharomyces Cerevisiae and E.coli datasets.
  • The biological coherence of REGNET's inferred networks was assessed.
  • REGNET demonstrated superior accuracy in detecting true gene associations compared to Pearson and Spearman correlation-based methods when validated against the E.coli transcriptional network.

Conclusions:

  • REGNET effectively generates gene association networks from gene expression data, calculating relationships simultaneously.
  • Model trees offer precise estimation of target gene values via linear regression functions, excelling at localized similarities.
  • Experimental results confirm the robust performance and accuracy of the REGNET approach.