Jove
Visualize
Contact Us

Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

19.5K
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
19.5K
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

4.4K
Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
4.4K
Cluster Sampling Method01:20

Cluster Sampling Method

13.5K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.5K

You might also read

Related Articles

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

Sort by
Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026
Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026
Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026
Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026
Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026
Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·2026
See all related articles
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 Experiment Video

Updated: Nov 9, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.2K

A hybrid multi-objective whale optimization algorithm for analyzing microarray data based on Apache Spark.

Amr Mohamed AbdelAziz1, Taysir Soliman2, Kareem Kamal A Ghany1,3

  • 1Faculty of Computers and Artificial Intelligence, Beni-Suef University, Egypt.

Peerj. Computer Science
|April 9, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm, Multi-Objective Whale Optimization Algorithm with Tabu Search (MOWOATS), for analyzing big data from microarrays. The algorithm efficiently clusters genes with similar expression profiles, demonstrating high accuracy and scalability.

Keywords:
Data miningDistributed and parallel computationMicroarray dataSwarm intelligence

More Related Videos

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.2K
The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
13:02

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

10.6K

Related Experiment Videos

Last Updated: Nov 9, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.2K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.2K
The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
13:02

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

10.6K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Microarray technology generates large-scale gene expression data, often classified as Big Data.
  • Analyzing this Big Data is crucial for identifying gene correlations and understanding biological processes.
  • Traditional clustering methods struggle with the complexity and volume of microarray data, necessitating advanced approaches.

Purpose of the Study:

  • To propose and evaluate a novel hybrid algorithm, Multi-Objective Whale Optimization Algorithm with Tabu Search (MOWOATS), for analyzing massive microarray datasets.
  • To adapt MOWOATS for parallel processing using Apache Spark over Hadoop clusters to handle Big Data efficiently.
  • To assess the quality and scalability of MOWOATS in clustering gene expression profiles.

Main Methods:

  • Development of a hybrid Multi-Objective Whale Optimization Algorithm with Tabu Search (MOWOATS).
  • Adaptation of MOWOATS for parallel computation on Hadoop clusters using Apache Spark.
  • Implementation of three evaluation functions for robust solution assessment.
  • Evaluation of clustering quality using Silhouette and Davies-Bouldin indices.

Main Results:

  • MOWOATS successfully clustered genes with similar expression profiles from massive microarray datasets.
  • The algorithm demonstrated high-quality clustering, with obtained clusters closely matching original data classes.
  • Scalability analysis showed that MOWOATS's running time was inversely proportional to the number of computing nodes, indicating efficient Big Data processing.

Conclusions:

  • MOWOATS is an efficient and scalable hybrid algorithm for analyzing Big Data generated by microarrays.
  • The parallel implementation on Spark over Hadoop enables effective handling of massive gene expression datasets.
  • The proposed method offers a robust solution for gene expression data clustering, aiding in the identification of biologically relevant gene patterns.