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

Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

60.0K
Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
60.0K

You might also read

Related Articles

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

Sort by
Same author

Optimisation and comparative extraction of safranal from <i>Crocus sativus</i> L. stigmas and commercial saffron products using response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS).

Natural product research·2026
Same author

Early Exposure to Orthopaedic Surgery.

Cureus·2024
Same author

Blockchain and Machine Learning Inspired Secure Smart Home Communication Network.

Sensors (Basel, Switzerland)·2023
Same author

Stener-Like Lesions in the Hand: A Qualitative Review.

Hand (New York, N.Y.)·2023
Same author

Monitoring Ambient Parameters in the IoT Precision Agriculture Scenario: An Approach to Sensor Selection and Hydroponic Saffron Cultivation.

Sensors (Basel, Switzerland)·2022
Same author

Visualization of Customized Convolutional Neural Network for Natural Language Recognition.

Sensors (Basel, Switzerland)·2022

Related Experiment Video

Updated: Mar 15, 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

8.1K

A Novel Hybrid Feature Selection Model for Classification of Neuromuscular Dystrophies Using Bhattacharyya

Divya Anand1, Babita Pandey2, Devendra K Pandey3

  • 1School of Computer Science and Engineering, Lovely Professional University, Chaheru, Punjab, India. divyaanand.y@gmail.com.

Interdisciplinary Sciences, Computational Life Sciences
|September 18, 2016
PubMed
Summary

Accurately classifying neuromuscular disorders is crucial for patient care. This study introduces a novel hybrid feature selection model using Bhattacharyya coefficient and genetic algorithms (GA) to identify key genes for improved diagnostic accuracy.

Keywords:
Bhattacharyya coefficientGenetic algorithmMicroarray dataNeuromuscular disordersRadial basis functionSupport vector machine

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.6K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.4K

Related Experiment Videos

Last Updated: Mar 15, 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

8.1K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.6K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.4K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Accurate classification of neuromuscular disorders is essential for effective patient treatment.
  • Microarray technology allows simultaneous monitoring of numerous gene expressions, but generates high-dimensional data with few samples.
  • Dimensionality reduction is needed to identify a small set of discriminative genes for disease classification.

Purpose of the Study:

  • To develop a novel hybrid feature selection model for accurate classification of neuromuscular disorders.
  • To identify a small subset of genes that effectively distinguishes between different types of neuromuscular disorders.
  • To improve the accuracy of neuromuscular disorder classification using gene expression data.

Main Methods:

  • A two-phase hybrid feature selection model integrating Bhattacharyya coefficient and genetic algorithm (GA).
  • Phase 1: Bhattacharyya coefficient used to select candidate genes by removing redundant ones.
  • Phase 2: GA applied to select the most discriminative gene subset, with fitness calculated using radial basis function support vector machine (RBF SVM).

Main Results:

  • The proposed hybrid algorithm was applied to two public microarray datasets for neuromuscular disorders.
  • The hybrid method demonstrated superior classification accuracy compared to individual Bhattacharyya coefficient and GA methods.
  • Performance was also better than a combined Bhattacharyya-GA approach using different classifiers for GA fitness calculation.

Conclusions:

  • The novel hybrid feature selection model effectively identifies discriminative genes for neuromuscular disorder classification.
  • The integration of Bhattacharyya coefficient and GA with RBF SVM offers enhanced classification accuracy.
  • This approach provides a promising tool for improving the diagnosis and understanding of neuromuscular disorders through gene expression analysis.