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Related Concept Videos

Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the population that is...
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
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.
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Workflow for generating competing hypothesis from models with parameter uncertainty.

David Gomez-Cabrero1, Albert Compte, Jesper Tegner

  • 1Department of Medicine, Karolinska Institutet , Unit of Computational Medicine, Centre for Molecular Medicine , Solna, Stockholm , Sweden.

Interface Focus
|June 7, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces an integrated workflow to analyze complex biological models, even with limited data. The approach extracts key behaviors and mechanistic hypotheses from uncertain mathematical models, like those in atherogenesis research.

Keywords:
bioinformaticscomputational biologysystems biology

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A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
13:54

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Published on: August 18, 2023

Area of Science:

  • Life Sciences
  • Computational Biology
  • Systems Biology

Background:

  • Biological models are often over-parameterized and under-constrained by data.
  • Previous analyses of complex models lacked an integrated approach from building to validation.

Purpose of the Study:

  • To develop an integrated workflow for analyzing underdetermined biological models.
  • To extract core behaviors and competing mechanistic hypotheses from complex models.

Main Methods:

  • Utilized optimization search algorithms.
  • Employed statistical machine-learning classification techniques.
  • Applied cluster-based analysis of state variables and parameters.

Main Results:

  • Successfully applied the workflow to a mathematical model of atherogenesis.
  • Identified key behaviors in the complex atherogenesis model despite parameter uncertainty.
  • Derived distinct mechanistic predictions from the model.

Conclusions:

  • An integrated workflow can effectively analyze complex biological models with uncertainty.
  • This methodology facilitates understanding and hypothesis generation from underdetermined models.
  • Enables robust biological insights despite limited quantitative data and parameter confidence.