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

Signs of Puberty01:27

Signs of Puberty

1.5K
Puberty is a critical phase, typically beginning between the ages of 8 and 13 in girls and 9 and 14 in boys, though timing can vary based on genetics, environmental factors, and overall health. This period is characterized by the development of secondary sexual characteristics and the attainment of reproductive potential. Endocrine changes underpin puberty, with hormonal surges of Luteinizing Hormone (LH) and Follicle-Stimulating Hormone (FSH) instigated by Gonadotropin-Releasing Hormone (GnRH)...
1.5K
Introduction to the Sign Test01:10

Introduction to the Sign Test

1.3K
The sign test is an important tool in nonparametric statistics, offering a straightforward yet effective method for analyzing matched pairs, nominal data, or hypotheses concerning the median of a population. It transforms data points into positive or negative signs, avoiding the need for assumptions about data distribution and instead focusing on the direction of change. It is particularly valuable when data does not conform to the normal distribution requirements of many parametric tests. For...
1.3K
Sign Convention01:30

Sign Convention

3.5K
When analyzing a beam subjected to various loads, it is crucial to understand the internal forces and moments generated within the structure. These internal forces can be broadly classified into normal forces, shear forces, and bending moments. To determine these forces and moments, we use the method of sections and apply a specific sign convention based on their direction and the side of the section being analyzed.
The normal force acts perpendicular to the beam's cross-section and can...
3.5K
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

1.9K
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
1.9K
Sign Test for Nominal Data01:12

Sign Test for Nominal Data

387
The sign test is a nonparametric method used to evaluate hypotheses about the median of a single sample or to compare the medians of two related samples. The sign test is particularly useful when dealing with nominal data, which includes distinct categories without an inherent order, such as names, labels, and preferences. Nominal data restricts statistical analysis to evaluating population proportions rather than mean or median values that require continuous data.
For example, consider a...
387
Introduction to Vital Signs01:25

Introduction to Vital Signs

7.8K
Vital signs are physiological measurements that help key into the status of the body's essential functions. These include body temperature, pulse rate, respiratory rate, and blood pressure, commonly abbreviated as T, P, R, and BP. Some healthcare settings also consider oxygen saturation (SpO2) and, in specific contexts, pain and level of consciousness as additional vital signs.
Vital signs help healthcare professionals assess an individual's well-being and detect any functional changes...
7.8K

You might also read

Related Articles

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

Sort by
Same author

A systematic review of machine learning on clinical MALDI-TOF MS.

Briefings in bioinformatics·2026
Same author

Multimodal approach to characterize surgically removed epileptogenic zone from patients with focal drug-resistant epilepsy: From operating room to wet lab.

Epilepsia open·2025
Same author

Addressing wide-data studies of gene expression microarrays with the Relevance Feature and Vector Machine.

Computers in biology and medicine·2025
Same author

Automated web-based typing of Clostridioides difficile ribotypes via MALDI-TOF MS.

BMC bioinformatics·2025
Same author

Predictive model of ibuprofen treatment failure in very preterm infants with patent ductus arteriosus using machine learning techniques.

Journal of perinatology : official journal of the California Perinatal Association·2025
Same author

Damage in thalamic projection to perilesional cortex as a prognostic biomarker for experimental post-traumatic epilepsy.

Epilepsia·2025

Related Experiment Video

Updated: Jan 27, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K

Sign-Consistency Based Variable Importance for Machine Learning in Brain Imaging.

Vanessa Gómez-Verdejo1, Emilio Parrado-Hernández1, Jussi Tohka2

  • 1Department of Signal Processing and Communications, Universidad Carlos III de Madrid, Leganés, Spain.

Neuroinformatics
|March 29, 2019
PubMed
Summary

This study introduces sign-consistency bagging (SCB), a novel method for identifying important brain imaging variables. SCB enhances classification accuracy and reproducibility in neuroimaging analysis.

Keywords:
Alzheimer’s DiseaseBaggingMRISchizophreniaSupport Vector MachinesVariable importance

More Related Videos

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

955
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

1.1K

Related Experiment Videos

Last Updated: Jan 27, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

955
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

1.1K

Area of Science:

  • Neuroimaging
  • Machine Learning
  • Biostatistics

Background:

  • Supervised classification in brain imaging faces challenges due to high dimensionality (more variables than subjects).
  • Accurate multivariate variable importance measures are crucial but difficult to derive in such scenarios.

Purpose of the Study:

  • To propose a novel multivariate variable importance measure, sign-consistency bagging (SCB).
  • To enhance SCB measures using transductive conformal analysis for heterogeneous data.
  • To develop a parametric hypothesis test for variable importance.

Main Methods:

  • Developed sign-consistency bagging (SCB) by analyzing weight sign consistency in linear support vector machine (SVM) ensembles.
  • Integrated transductive conformal analysis to refine importance measures for heterogeneous datasets.
  • Derived a parametric hypothesis test for assessing variable importance.

Main Results:

  • SCB demonstrated superior reproducibility and classification accuracy compared to t-test and SVM-based methods.
  • The enhanced SCB measures proved effective, particularly with heterogeneous neuroimaging data.
  • The proposed hypothesis test provided a robust statistical framework for variable importance.

Conclusions:

  • Sign-consistency bagging (SCB) offers a powerful and reproducible approach for variable selection in high-dimensional neuroimaging data.
  • The integration of transductive conformal analysis and hypothesis testing further strengthens SCB's utility.
  • SCB advances the application of machine learning in brain imaging analysis.