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Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Steps in the Modeling Process01:14

Steps in the Modeling Process

Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
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Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...

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An Open Source Technology Platform to Manufacture Hydrogel-Based 3D Culture Models in an Automated and Standardized Fashion
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Published on: March 31, 2022

Modeling, informatics, and the quest for reproducibility.

W Patrick Walters

    Journal of Chemical Information and Modeling
    |June 14, 2013
    PubMed
    Summary

    Reproducibility in molecular modeling and cheminformatics is challenging due to unavailable software and inaccessible data. Establishing clear guidelines is crucial for improving the reliability of published scientific research.

    Area of Science:

    • Computational chemistry
    • Cheminformatics
    • Molecular modeling

    Background:

    • Scientific publications in journals like the Journal of Chemical Information and Modeling offer valuable data.
    • Reproducibility of research in molecular modeling and cheminformatics is frequently hindered by challenges.

    Discussion:

    • Software accessibility and data format issues impede the replication of published studies.
    • Major journals in molecular modeling and cheminformatics currently lack established guidelines for reproducible research.

    Key Insights:

    • Lack of accessible software and data formats are primary barriers to reproducibility.
    • Absence of standardized guidelines across leading journals exacerbates the reproducibility crisis.

    Outlook:

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    • Implementing clear guidelines for reproducible research is essential for scientific progress.
    • Future efforts should focus on enhancing data accessibility and software availability in publications.