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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Distribution Reliability and Automation01:25

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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Per-Unit Sequence Models01:26

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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Multicompartment Models: Overview01:14

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Pharmacodynamic Models: Overview01:27

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Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
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Related Experiment Video

Updated: Mar 5, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Discovering feature relevancy and dependency by kernel-guided probabilistic model-building evolution.

Nestor Rodriguez1, Sergio Rojas-Galeano1

  • 1Universidad Distrital FJC, School of Engineering, Bogota, Colombia.

Biodata Mining
|March 24, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new wrapper method for identifying disease biomarkers and their interactions. The approach effectively discovers genetic dependencies and relevant features, aiding biomedical research and potentially reducing experimental costs.

Keywords:
Dependency estimationEpistasisFeature selectionHepatitis datasetRelevancy discoveryVisual programming tools

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genetics

Background:

  • Identifying disease biomarkers is crucial for targeted research and cost savings.
  • Understanding biomarker dependencies can reveal complex genetic interactions like epistasis.
  • Existing wrapper methods primarily focus on feature relevancy, not simultaneous discovery of relevancy and dependencies.

Purpose of the Study:

  • To develop a novel wrapper method for simultaneously identifying relevant biomarkers and their dependencies.
  • To guide the search for a probabilistic model of marginal and interacting effects using a weighted kernel classifier.
  • To enhance biomarker discovery for disease etiology discrimination.

Main Methods:

  • A new wrapper method utilizing a weighted kernel classifier.
  • Development of a probabilistic model for simultaneous marginal and interacting effects.
  • Evaluation through three empirical studies: human genetic problems, benchmark classification, and a hepatitis dataset.

Main Results:

  • Successfully discovered complex epistatic effects in 4 out of 5 human genetic problems, outperforming a baseline method.
  • Achieved comparable prediction accuracy to baseline techniques in benchmark tasks while identifying smaller feature subsets.
  • Findings on biomarker relevancy and dependency in a hepatitis dataset were corroborated by existing medical literature.

Conclusions:

  • The method offers significant mining advantages but presents higher computational complexity for large datasets.
  • Future work could extend probabilistic assumptions to continuous distributions and higher-order interactions.
  • Advocates for visual graphical software tools to empower biodata researchers in experiment design and analysis.