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

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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.
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Updated: Nov 24, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

Compositional zero-inflated network estimation for microbiome data.

Min Jin Ha¹, Junghi Kim², Jessica Galloway-Peña³

¹Department of Biostatistics, University of Texas MD Anderson Cancer Center, 1400 Pressler St., Houston, TX, USA. MJHa@mdanderson.org.

BMC Bioinformatics

|December 29, 2020

Summary

This summary is machine-generated.

We developed a new method, COmpositional Zero-Inflated Network Estimation (COZINE), to accurately infer microbial networks from complex microbiome data. COZINE effectively addresses challenges like compositional and zero-inflated data, improving ecological relationship insights.

Keywords:

Compositional data Graphical model Microbiome Network Zero-inflation

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Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

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Area of Science:

Microbiome research
Computational biology
Statistical ecology

Background:

Microbiome network estimation is crucial for understanding ecological relationships.
High-throughput microbiome data present statistical challenges: compositional nature and zero-inflation.
Existing methods struggle with these data characteristics.

Purpose of the Study:

To introduce a novel method, COmpositional Zero-Inflated Network Estimation (COZINE), for microbial network inference.
To address the compositional and zero-inflated properties of microbiome data.
To provide a computationally scalable solution for network estimation.

Main Methods:

Utilized the multivariate Hurdle model within the COZINE framework.
Inferred sparse conditional dependencies considering continuous, binary, and mixed data representations.
Developed a method that maintains computational scalability.

Main Results:

COZINE demonstrated superior performance in capturing diverse microbial relationships compared to existing methods.
Simulations confirmed the method's effectiveness in various scenarios.
Applied COZINE to analyze the oral microbiome network in leukemic patients.

Conclusions:

COZINE successfully addresses key challenges in microbiome network estimation.
The method effectively discovers diverse dependence relationships within microbial communities.
COZINE is publicly available for broader scientific application.