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

Longitudinal Studies01:26

Longitudinal Studies

421
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
421
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

505
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
505
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

6.8K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
6.8K
Multiple Regression01:25

Multiple Regression

3.7K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.7K
Longitudinal Research02:20

Longitudinal Research

13.0K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
13.0K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.8K

You might also read

Related Articles

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

Sort by
Same author

Transcriptomics of type 1 diabetes progression: a validation study in newly diagnosed patients.

EBioMedicine·2026
Same author

Clarifying the scope and capabilities of ROTS in differential expression analysis.

Bioinformatics (Oxford, England)·2026
Same author

Joint modeling of longitudinal and time-to-event data for dynamic disease risk prediction using proteomics.

Protein science : a publication of the Protein Society·2026
Same author

REACTOR: REgulon Activity analysis and Comparison Tool for single-cell transcriptOmics Research.

Bioinformatics (Oxford, England)·2026
Same author

Therapeutic TG2 inhibition reverses systemic multiomic dysregulation in celiac disease.

BMC medicine·2026
Same author

Testosterone Exposure During Fetal Masculinization Programming Window Determines the Kidney Size in Adult Mice.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2026

Related Experiment Video

Updated: Dec 30, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.7K

Likelihood contrasts: a machine learning algorithm for binary classification of longitudinal data.

Riku Klén1,2, Markku Karhunen1, Laura L Elo3

  • 1Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.

Scientific Reports
|January 25, 2020
PubMed
Summary
This summary is machine-generated.

We developed a new machine learning method, Likelihood Contrasts (LC), for analyzing biomedical longitudinal data with unaligned time points. LC demonstrates superior accuracy and robustness compared to existing methods in simulations and real-world applications.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

372

Related Experiment Videos

Last Updated: Dec 30, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.7K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

372

Area of Science:

  • Biomedical research
  • Machine learning applications
  • Longitudinal data analysis

Background:

  • Machine learning is increasingly used in biomedical research.
  • Few methods effectively analyze longitudinal data (multiple samples over time).
  • Existing methods often require time-aligned measurements, which is rare in practice.

Purpose of the Study:

  • Introduce a novel, robust machine learning method for longitudinal data analysis.
  • Address the challenge of unaligned time points in real-world biomedical studies.
  • Provide a computationally efficient and user-friendly tool for researchers.

Main Methods:

  • Developed Likelihood Contrasts (LC), a binary classification method.
  • Utilized linear mixed models for data modeling.
  • Employed log-likelihood for decision-making.
  • Validated against existing methods using simulated and real datasets.

Main Results:

  • LC outperformed existing methods in accuracy across four simulated datasets.
  • Real-world datasets confirmed the robust performance of the LC method.
  • LC proved to be computationally efficient and easy to implement.

Conclusions:

  • Likelihood Contrasts (LC) offers a robust and accurate solution for analyzing unaligned longitudinal biomedical data.
  • The method enhances the applicability of machine learning in complex, real-world research scenarios.
  • LC provides a valuable, efficient tool for biomedical researchers dealing with time-course data.