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Related Concept Videos

Piecewise-Defined Functions01:28

Piecewise-Defined Functions

Piecewise defined functions are mathematical models where different expressions define a function over distinct intervals of the domain. These functions are useful for representing systems with varying behaviors depending on input values.For example, the function:  uses a linear rule for inputs less than or equal to –1 and a quadratic rule for values greater than –1. Although it has two formulas, it still defines a single function.Another common type is the absolute value function, given...
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
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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...
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
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Classification of Signals01:30

Classification of Signals

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Related Experiment Video

Updated: Jun 5, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Published on: February 9, 2017

Classifying short gene expression time-courses with Bayesian estimation of piecewise constant functions.

Christoph Hafemeister1, Ivan G Costa, Alexander Schönhuth

  • 1Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany. hafemeis@molgen.mpg.de

Bioinformatics (Oxford, England)
|January 27, 2011
PubMed
Summary

This study introduces a novel method for analyzing short gene expression time-course data, improving toxic compound classification accuracy and significantly reducing computational time. The new approach models time-courses using piecewise constant functions and Hidden Markov Models for efficient analysis.

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Short time-course analysis is crucial in molecular biology, with 90% of experiments having nine or fewer time-points.
  • Applications include gene regulation, treatment response prediction, and toxic compound classification.
  • Classifying toxic compounds involves irregular time-series data, necessitating local, gapped alignment.

Purpose of the Study:

  • To develop an efficient method for analyzing short, multivariate gene expression time-courses, particularly for toxicology.
  • To improve classification accuracy and reduce computational time compared to existing methods like SCOW.
  • To model time-courses using piecewise constant functions and Hidden Markov Models.

Main Methods:

  • Modeled time-courses as piecewise constant functions using left-right Hidden Markov Models.
  • Employed a Bayesian approach for parameter estimation and inference.
  • Applied the method to toxicology and stress response data classification.

Main Results:

  • Achieved a 7% improvement in classifying toxicology data.
  • Achieved a 4% improvement in classifying stress response data.
  • Reduced running times by at least a factor of 140, crucial for real-time applications.

Conclusions:

  • Modeling time-courses with reduced complexity via Hidden Markov Models significantly enhances classification performance.
  • The proposed method offers substantial improvements in both accuracy and computational efficiency.
  • A Python package for the described methods is available.