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Updated: Jun 9, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Discovering graphical Granger causality using the truncating lasso penalty.

Ali Shojaie1, George Michailidis

  • 1Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109, USA. shojaie@umich.edu

Bioinformatics (Oxford, England)
|September 9, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces truncating lasso, a new method for identifying gene regulatory networks from time-course gene expression data. It accurately estimates causal relationships and time lags, improving upon existing lasso-type estimators.

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Last Updated: Jun 9, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Area of Science:

  • Systems Biology
  • Genomics
  • Bioinformatics

Background:

  • Biological systems rely on complex interactions between components for cellular functions.
  • Understanding gene regulatory interactions is crucial for systems biology research.
  • Time-course gene expression data offers insights into gene regulation dynamics.

Purpose of the Study:

  • To propose a novel penalization method, truncating lasso, for estimating causal relationships in gene expression data.
  • To improve the accuracy of time series analysis and identify time lags in gene regulation.
  • To provide an efficient algorithm for parameter estimation in gene regulatory network inference.

Main Methods:

  • Development of the truncating lasso penalty for time-course gene expression data.
  • Implementation of an efficient algorithm for model parameter estimation.
  • Consistent discovery of causal relationships in high-dimensional settings (large p, small n).

Main Results:

  • The truncating lasso method accurately determines time series order and improves lasso-type estimator performance.
  • The method provides information on time lags between transcription factor activation and gene effects.
  • Favorable performance demonstrated in both simulated and real gene expression data.

Conclusions:

  • Truncating lasso is an effective method for inferring gene regulatory interactions from time-course data.
  • The method enhances the understanding of cellular mechanisms by revealing causal relationships and temporal dynamics.
  • The R-package 'grangerTlasso' is available for implementing this novel approach.