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

Uncertainty: Overview00:59

Uncertainty: Overview

1.4K
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
1.4K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.2K
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...
1.2K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.6K
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...
1.6K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

6.9K
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).
6.9K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.2K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.2K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

10.0K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
10.0K

You might also read

Related Articles

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

Sort by
Same author

Uniform zinc oxide nanowire arrays grown on nonepitaxial surface with general orientation control.

Nano letters·2013
Same author

[American head and neck surgery progress of in 2012].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery·2013
Same author

A compact thermo-optical multimode-interference silicon-based 1 × 4 nano-photonic switch.

Optics express·2013
Same author

Experimental demonstration of 110-Gb/s unsynchronized band-multiplexed superchannel coherent optical OFDM/OQAM system.

Optics express·2013
Same author

Potentially functional variants of p14ARF are associated with HPV-positive oropharyngeal cancer patients and survival after definitive chemoradiotherapy.

Carcinogenesis·2013
Same author

Enhanced molecular transport in hierarchical silicalite-1.

Langmuir : the ACS journal of surfaces and colloids·2013

Related Experiment Video

Updated: Jan 3, 2026

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

A new feature selection method based on symmetrical uncertainty and interaction gain.

Xiaohui Lin1, Chao Li1, Weijie Ren1

  • 1School of Computer Science & Technology, Dalian University of Technology, 116024, Dalian, China.

Computational Biology and Chemistry
|November 22, 2019
PubMed
Summary
This summary is machine-generated.

A new method, Interaction Gain - Recursive Feature Elimination (IG-RFE), improves biological data analysis by evaluating both individual molecule relevance and interactions. This approach enhances accuracy, sensitivity, specificity, and stability in understanding physiological and pathological changes.

Keywords:
Biological data analysisFeature selectionInteraction gain

More Related Videos

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

2.9K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K

Related Experiment Videos

Last Updated: Jan 3, 2026

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
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

2.9K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Understanding biological complexity requires defining key information from large datasets.
  • Physiological and pathological changes are influenced by molecule interactions.
  • Analyzing individual molecules and their interactions offers a comprehensive view of organisms.

Purpose of the Study:

  • To propose a novel feature selection method, Interaction Gain - Recursive Feature Elimination (IG-RFE).
  • To enhance the evaluation of feature importance in biological data by integrating individual feature relevance and feature interactions.
  • To improve the accuracy and comprehensiveness of biological data analysis.

Main Methods:

  • Developed the Interaction Gain - Recursive Feature Elimination (IG-RFE) method.
  • Utilized symmetrical uncertainty to measure feature-class label relevance.
  • Calculated average normalized interaction gain to assess feature interactions.
  • Iteratively removed less important features based on combined relevance and interaction scores.

Main Results:

  • IG-RFE demonstrated superior performance compared to seven other feature selection methods (MIFS, mRMR, CMIM, ReliefF, FCBF, PGVNS, SVM-RFE).
  • The method showed improvements in accuracy, sensitivity, specificity, and stability across eleven public datasets.
  • The integration of individual feature discriminative ability and feature interactions proved effective for feature importance evaluation.

Conclusions:

  • The proposed IG-RFE method offers a more effective approach to feature selection in biological data analysis.
  • Integrating feature relevance and interaction information leads to a better understanding of biological systems.
  • IG-RFE provides a robust tool for identifying critical molecular players in physiological and pathological processes.