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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

151
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.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
151
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

156
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
156
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

285
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
285
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

135
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
135
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

280
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
280
Quadratic Models01:23

Quadratic Models

14
Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
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Related Experiment Video

Updated: Oct 29, 2025

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Computationally scalable regression modeling for ultrahigh-dimensional omics data with ParProx.

Seyoon Ko1, Ginny X Li2, Hyungwon Choi2

  • 1Department of Statistics, Seoul National University, Republic of Korea.

Briefings in Bioinformatics
|July 13, 2021
PubMed
Summary

ParProx optimizes group lasso regression for ultrahigh-dimensional omics data, enabling scalable and interpretable genotype-phenotype association analysis. This method integrates biological priors for variable selection in time-to-event and classification tasks.

Keywords:
latent group lassoparallel computingproximal gradientsparse regressionultrahigh-dimensional omics data

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Ultrahigh-dimensional omics data analysis traditionally relies on univariate methods.
  • There's a growing need for multivariable association analysis to understand genotype-phenotype relationships.
  • Existing methods struggle with the scale and complexity of modern omics datasets.

Purpose of the Study:

  • To introduce ParProx, a novel implementation for group lasso regression.
  • To enable scalable and interpretable multivariable association analysis for ultrahigh-dimensional omics data.
  • To integrate biological priors into variable selection for enhanced model interpretability.

Main Methods:

  • ParProx optimizes overlapping and non-overlapping group lasso regression models.
  • It employs latent variable group representation for parallel or distributed computing.
  • Variable selection is guided by biological priors, such as genomic regions and pathways.

Main Results:

  • Simulation studies demonstrate ParProx's scalability, especially with graphics processing units.
  • The tool successfully fits multivariable models for ultrahigh-dimensional data.
  • Application to omics datasets yielded interpretable results linked to biological groups.

Conclusions:

  • ParProx offers a scalable and interpretable solution for genotype-phenotype association studies.
  • It facilitates routine multivariable analysis in genomics and multi-omics research.
  • The method addresses computational challenges in analyzing large-scale omics data.