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 Experiment Videos

A unified framework for finding differentially expressed genes from microarray experiments.

Jahangheer S Shaik1, Mohammed Yeasin

  • 1Department of Electrical and Computer Engineering, CVPIA Lab, University of Memphis, Memphis, TN-38152, USA. jshaik@memphis.edu

BMC Bioinformatics
|September 20, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Lateral orbitofrontal cortex encodes presence of risk and subjective risk preference during decision-making.

Cerebral cortex (New York, N.Y. : 1991)·2025
Same author

Flexible imputation toolkit for electronic health records.

Scientific reports·2025
Same author

Auto-Rad: End-to-End Report Generation from Lumber Spine MRI Using Vision-Language Model.

Journal of clinical medicine·2024
Same author

Comparative genomics of Giardia duodenalis sub-assemblage AI beaver (Be-2) and human (WB-C6) strains show remarkable homozygosity, sequence similarity, and conservation of VSP genes.

Scientific reports·2024
Same author

Lateral Orbitofrontal Cortex Encodes Presence of Risk and Subjective Risk Preference During Decision-Making.

bioRxiv : the preprint server for biology·2024
Same author

Unraveling Spatial-Spectral Dynamics of Speech Categorization Speed Using Convolutional Neural Networks.

Brain sciences·2023
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

This study introduces a unified framework for robustly identifying differentially expressed genes (DEGs) in microarray data. The novel approach outperforms existing methods in both simulated and real-world datasets.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Microarray data analysis is crucial for identifying differentially expressed genes (DEGs).
  • Existing gene selection methods may lack robustness and comprehensive validation.
  • A unified framework is needed for reliable DEG identification.

Purpose of the Study:

  • To present a unified framework for robustly finding DEGs from microarray data.
  • To integrate gene ranking, significance analysis, and validation into a cohesive system.
  • To demonstrate the framework's superiority over existing algorithms.

Main Methods:

  • A three-module framework: gene ranking (two-way clustering, combined adaptive ranking), significance analysis (R-test, Fisher's omnibus criterion, FDR analysis), and three-fold validation (FDR analysis, clustering, visualization).

Related Experiment Videos

  • Comparison with t-statistics, SAM, Adaptive Ranking, Combined Adaptive Ranking, and Two-way Clustering.
  • Evaluation on 50 simulated datasets and 6 real-world datasets (3 cancer, 3 Parkinson's).

    • Main Results:

    • The unified framework demonstrated superior performance compared to established algorithms across simulated and real datasets.
    • Validation confirmed the robustness, relevance, and scalability of the selected DEGs.
    • The approach showed significant improvement in identifying DEGs in both two-sample and multi-sample experiments.

    Conclusions:

    • The unified framework provides a robust method for selecting DEGs in microarray experiments.
    • Integrating diverse ranking, significance testing, and validation enhances DEG identification accuracy.
    • The framework is effective and scalable for various microarray data types, outperforming existing methods.