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Related Concept Videos

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
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...
Decision Making01:20

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Related Experiment Video

Updated: May 9, 2026

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

A decision-theory approach to interpretable set analysis for high-dimensional data.

Simina M Boca1, Héctor Céorrada Bravo, Brian Caffo

  • 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, U.S.A.

Biometrics
|August 6, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel decision-theory approach for analyzing gene sets, estimating the fraction of non-null variables. The atomic false discovery rate (afdr) provides a coherent framework for set analysis, improving interpretability.

Keywords:
Atomic false discovery rateGene-setsHypothesis testingSet-level inference

Related Experiment Videos

Last Updated: May 9, 2026

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

Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • High-dimensional data analysis requires identifying significant predefined sets, such as gene sets enriched with differentially expressed genes.
  • Existing methods struggle with overlapping annotations and interpreting p-values in set-based analyses.

Purpose of the Study:

  • To develop a new decision-theory framework for gene set analysis.
  • To estimate the fraction of non-null variables within biological sets.
  • To introduce a novel metric, the atomic false discovery rate (afdr), for robust set enrichment analysis.

Main Methods:

  • Proposed a decision-theory approach focusing on estimating the proportion of non-null variables in a set.
  • Introduced "atoms": non-overlapping subsets derived from original annotations.
  • Developed the atomic false discovery rate (afdr) and proved thresholding it is optimal.

Main Results:

  • The proposed method offers a coherent and interpretable framework for set analysis.
  • Addresses challenges of overlapping annotations and p-value interpretation.
  • Demonstrated effectiveness through simulations and analyses of gene-set and brain ROI data.

Conclusions:

  • The decision-theory approach with afdr provides a robust method for high-dimensional set enrichment analysis.
  • Improves upon existing methods by handling annotation overlaps and enhancing interpretability.
  • Offers a valuable tool for biological data interpretation in genomics and neuroimaging.