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Related Concept Videos

Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Weighted Mean00:57

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

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Related Experiment Videos

Weighted-LASSO for structured network inference from time course data.

Camille Charbonnier1, Julien Chiquet, Christophe Ambroise

  • 1University of Evry-Val-d'Essonne. camille.charbonnier@genopole.cnrs.fr

Statistical Applications in Genetics and Molecular Biology
|March 4, 2010
PubMed
Summary
This summary is machine-generated.

We developed a new weighted-LASSO method to analyze gene expression data from regulatory networks. This approach uses prior network structure to improve the inference of gene interactions.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Gene expression data provides insights into cellular processes.
  • Understanding gene regulatory networks is crucial for biology.
  • Inferring network structures from time-course data is challenging.

Purpose of the Study:

  • To present a novel weighted-LASSO method for inferring parameters of vector auto-regressive models.
  • To incorporate prior network connectivity structures into the inference process.
  • To analyze time-course gene expression data from directed gene-to-gene regulation networks.

Main Methods:

  • Utilized a weighted-LASSO regression technique.
  • Employed first-order vector auto-regressive models.
  • Incorporated prior structural information of connectivity, either from biological knowledge or inferred.
  • Applied structure-based penalization for parameter inference.

Main Results:

  • Demonstrated the method's performance on synthetic datasets.
  • Validated the approach on two well-characterized regulatory networks: yeast cell cycle and E. coli S.O.S. DNA repair.
  • Showcased the effectiveness of structure-based penalization in inferring network parameters.

Conclusions:

  • The weighted-LASSO method effectively infers parameters for gene regulatory networks.
  • Prior network structure significantly aids in the accurate inference of gene interactions.
  • The method is applicable to both known and unknown biological network structures.