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Related Concept Videos

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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

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...
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...

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Related Experiment Video

Updated: Jul 19, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

BEENE: deep learning-based nonlinear embedding improves batch effect estimation.

Md Ashiqur Rahman1,2, Abdullah Aman Tutul1,3, Mahfuza Sharmin4

  • 1Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh.

Bioinformatics (Oxford, England)
|August 10, 2023
PubMed
Summary

Batch Effect Estimation using Nonlinear Embedding (BEENE) addresses challenges in single-cell RNA sequencing data by offering a robust method for detecting and quantifying batch effects. This deep learning approach improves data integration and interpretation beyond traditional methods like PCA.

Related Experiment Videos

Last Updated: Jul 19, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Analyzing large-scale single-cell RNA sequencing (scRNA-seq) datasets is hindered by batch effects, which are systematic variations arising from different experimental conditions or technologies.
  • Accurate detection and correction of batch effects are crucial for integrating datasets and drawing reliable biological conclusions from scRNA-seq data.
  • Existing methods, such as Principal Component Analysis (PCA), often struggle with complex, nonlinear batch effects, limiting their effectiveness.

Purpose of the Study:

  • To develop a novel deep learning-based method for robust batch effect estimation in scRNA-seq data.
  • To create a lower-dimensional embedding that effectively captures and distinguishes between biological variation and technical batch effects.
  • To provide a tool that is more sensitive and precise than linear methods for detecting and quantifying batch effects, accommodating both linear and nonlinear patterns.

Main Methods:

  • Implementation of Batch Effect Estimation using Nonlinear Embedding (BEENE), a deep nonlinear auto-encoder network.
  • Simultaneous learning of batch and biological variables within the scRNA-seq data.
  • Generation of an alternative low-dimensional embedding optimized for batch effect detection and quantification.

Main Results:

  • BEENE generates embeddings that are more robust and sensitive for detecting and quantifying batch effects compared to PCA.
  • The method was successfully validated on simulated datasets and diverse biological datasets, including mouse embryogenesis cells, peripheral blood mononuclear cells, and pancreatic islet cells.
  • BEENE effectively handles both linear and nonlinear batch effects, offering improved data integration capabilities.

Conclusions:

  • BEENE provides a powerful new approach for addressing batch effects in scRNA-seq data analysis.
  • The deep nonlinear embedding strategy enhances the accuracy and reliability of data integration and biological interpretation.
  • BEENE represents a significant advancement in computational tools for single-cell genomics.