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Basic Continuous Time Signals01:22

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Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell

Enzo Acerbi1, Teresa Zelante, Vipin Narang

  • 1Singapore Immunology Network (SIgN), A*STAR, 8A Biomedical Grove, Immunos Building, Level 4 138648, Singapore. enzoace@gmail.com.

BMC Bioinformatics
|December 16, 2014
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Summary
This summary is machine-generated.

Continuous time Bayesian networks accurately reconstruct gene regulatory networks from time-course data, outperforming existing methods. This approach is robust even with unevenly spaced or dense measurements, offering new insights into cellular differentiation.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Gene regulatory network (GRN) dynamics are typically studied using time-course data.
  • Current GRN reconstruction methods often assume synchronous system evolution at fixed time points.
  • Advances in omics data generation allow for continuous-time modeling of biological systems.

Purpose of the Study:

  • To introduce Continuous Time Bayesian Networks (CTBNs) for GRN reconstruction from time-course expression data.
  • To compare the performance of CTBNs against established methods like Dynamic Bayesian Networks (DBNs) and Granger Causality Analysis (GCA).
  • To apply CTBNs for elucidating regulatory mechanisms in murine T helper 17 (Th17) cell differentiation.

Main Methods:

  • Development and application of Continuous Time Bayesian Networks (CTBNs).
  • Comparative analysis using simulated datasets of varying sizes and time-course densities (evenly and unevenly spaced).
  • Validation on the IRMA experimental dataset and application to murine Th17 cell differentiation data.

Main Results:

  • CTBNs demonstrated superior accuracy in learning regulatory interactions compared to DBNs and GCA across various network sizes.
  • CTBN performance degraded smoothly with increasing network size.
  • CTBNs outperformed DBNs across all tested time granularities and GCA for dense time series.
  • CTBNs and GCA showed robustness with unevenly spaced time series, unlike DBNs.

Conclusions:

  • CTBNs are effective for GRN reconstruction in both small and large networks, especially with non-uniformly distributed time-course data.
  • Application to murine Th17 cell differentiation revealed known mechanisms and identified novel insights, including potential autocrine loops.
  • CTBNs offer a powerful and feasible approach for modeling complex gene regulatory dynamics.