Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

694
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
694
Overview of Microsoft Excel as a Data Analysis Tool01:13

Overview of Microsoft Excel as a Data Analysis Tool

1.5K
Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and...
1.5K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.4K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.4K
Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

928
Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
928
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

327
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
327
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

37.0K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
37.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Self-explaining artificial intelligence for the classification of B cell non-Hodgkin lymphoma: A diagnostic decision support study.

PLoS medicine·2026
Same author

Investigation of polygenic risk scores and subphenotypes in social anxiety disorder.

Translational psychiatry·2026
Same author

Copy number variant analysis by exome sequencing is an effective approach to optimize diagnostic yield for developmental disorders-the DDD-Africa study.

European journal of human genetics : EJHG·2026
Same author

Polygenic risk scores: en route to clinical practice.

Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V·2026
Same author

Polygenic scores in psychiatric research and clinical practice.

Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V·2026
Same author

Polygenic risk scores in clinical applications - opportunities and challenges.

Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V·2026

Related Experiment Video

Updated: Jan 23, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

923

PEDIA: prioritization of exome data by image analysis.

Tzung-Chien Hsieh1,2,3, Martin A Mensah2,3, Jean T Pantel1,2,3

  • 1Institute of Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany.

Genetics in Medicine : Official Journal of the American College of Medical Genetics
|June 6, 2019
PubMed
Summary

Artificial intelligence analyzes facial photos to improve genomic variant interpretation. This AI-driven approach significantly enhances the accuracy of diagnosing genetic disorders from exome data.

Keywords:
computer visiondeep learningdysmorphologyexome diagnosticsvariant prioritization

More Related Videos

Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma
08:53

Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma

Published on: June 10, 2017

10.4K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.3K

Related Experiment Videos

Last Updated: Jan 23, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

923
Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma
08:53

Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma

Published on: June 10, 2017

10.4K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.3K

Area of Science:

  • Genomics
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Phenotype information is vital for interpreting genomic variants.
  • Currently, phenotype data requires manual encoding by experts for bioinformatics workflows.

Purpose of the Study:

  • To introduce an AI-driven approach using portrait photographs for clinical exome data interpretation.
  • To assess the impact of computer-assisted image analysis on diagnostic yield.

Main Methods:

  • Developed an AI approach utilizing frontal photographs for exome data analysis.
  • Evaluated the method on a cohort of 679 individuals with 105 monogenic disorders.
  • Compiled frontal photos, clinical features, and causative variants, simulating diverse ethnic exomes.

Main Results:

  • Computer-assisted photo analysis improved top 1 accuracy by over 20-89%.
  • Top 10 accuracy for identifying disease-causing genes increased by more than 5-99%.
  • Deep-learning algorithms quantified phenotypic similarity (PP4 criterion).

Conclusions:

  • AI-powered image analysis can quantify phenotypic similarity.
  • This approach significantly advances the performance of exome analysis bioinformatics pipelines.