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Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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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.
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Updated: May 28, 2025

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
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Mixtures of logistic normal multinomial regression models for microbiome data.

Wenshu Dai1, Yuan Fang2, Sanjeena Subedi3

  • 1Department of Mathematics and Statistics, Binghamton University, Binghamton, NY, USA.

Journal of Applied Statistics
|February 14, 2025
PubMed
Summary

This study introduces a new method for clustering microbiome data, treating it as compositional data. The approach helps analyze bacterial abundance in relation to factors like diet and age.

Keywords:
Clusteringlogistic-normal multinomial modelmicrobiome dataregression-based mixture modelsvariational Gaussian approximation

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Area of Science:

  • Bioinformatics
  • Microbiome analysis
  • Statistical modeling

Background:

  • Microbiome taxa count data from 16S rRNA sequencing is high-dimensional and compositional.
  • Analyzing this data is challenging due to its simplex confinement and influence from covariates.
  • Existing methods may not fully capture the complexity of microbiome data relationships.

Purpose of the Study:

  • To develop a novel regression-based mixture model for clustering microbiome data.
  • To enable exploration of relationships between bacterial abundance and covariates within identified clusters.
  • To improve the accuracy and efficiency of parameter estimation for microbiome data analysis.

Main Methods:

  • Developed regression-based mixtures of logistic normal multinomial models.
  • Utilized variational Gaussian approximations (VGA) for parameter estimation.
  • Applied the method to both simulated and real microbiome datasets.

Main Results:

  • The proposed models effectively categorize samples into homogeneous subpopulations.
  • Demonstrated the ability to explore covariate relationships within identified bacterial groups.
  • VGA framework enhanced accuracy and efficiency in parameter estimation.

Conclusions:

  • The novel clustering approach is effective for microbiome data analysis.
  • The method facilitates understanding of biological and environmental influences on bacterial abundance.
  • This approach offers a robust framework for compositional data analysis in bioinformatics.