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 Video

Updated: May 13, 2026

Quantitative Analysis of Cancer Metastasis using an Avian Embryo Model
08:40

Quantitative Analysis of Cancer Metastasis using an Avian Embryo Model

Published on: May 30, 2011

Breast Cancer Biomarker Discovery Using an Enhanced Quantum-Based Avian Navigation Optimizer and Ensemble Learning

Morteza Rakhshaninejad, Mohammad Fathian, Navid Yazdanjue

    IEEE Transactions on Computational Biology and Bioinformatics
    |May 11, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    Mouse Models of Cancer Study02:43

    Mouse Models of Cancer Study

    Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
    The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

    You might also read

    Related Articles

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

    Sort by
    Same author

    Reliable Molecular Retrieval from Mass Spectra Using Conformal Prediction.

    Journal of chemical information and modeling·2026
    Same author

    Hybrid machine learning approach for predicting compressive strength of sustainable concrete incorporating palm oil fuel ash.

    Scientific reports·2026
    Same author

    Pricing and procurement strategies in the relief supply chain via bidirectional option contract.

    PloS one·2026
    Same author

    Machine learning prediction of metabolic-associated fatty liver disease in type 2 diabetes: Emphasizing data imputation and feature selection.

    PloS one·2026
    Same author

    Efficient estimation of proton exchange membrane fuel cells parameters using a hybrid swarm intelligent algorithm.

    Scientific reports·2026
    Same author

    Rank charged system search algorithm for optimization and operations research.

    Scientific reports·2026
    Same journal

    A Multi-Modal Framework for Phage-Host Interaction Prediction Using Multi-View Contrastive Learning.

    IEEE transactions on computational biology and bioinformatics·2026
    Same journal

    Decoding Gene-Disease Associations with Computational Methods: A Survey.

    IEEE transactions on computational biology and bioinformatics·2026
    Same journal

    A Competitive Coevolution-based Cancer Driver Pathway Identification Algorithm for Maximizing Coverage, Mutual Exclusivity, and Subnet Importance.

    IEEE transactions on computational biology and bioinformatics·2026
    Same journal

    Prediction of GO Terms Based on Partitioning PPI Networks into Highly Connected Components.

    IEEE transactions on computational biology and bioinformatics·2026
    Same journal

    Modeling and Tracking of Heterogeneous Cell Populations via Open Multi-Agent Systems.

    IEEE transactions on computational biology and bioinformatics·2026
    Same journal

    Parameter Efficient Deep Learning Models for Multi-Target Binding Affinity and hERG Cardiotoxicity Prediction.

    IEEE transactions on computational biology and bioinformatics·2026
    See all related articles

    This study introduces a new algorithm, Ensemble-Based Logical Binary QANA (LBQANA_En), for improved breast cancer early detection. It accurately identifies key biomarkers, significantly reducing false positives and enhancing diagnostic accuracy.

    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Machine Learning

    Background:

    • Breast cancer early detection is critical but challenged by high false positives and complex gene datasets.
    • Traditional differential evolution methods lack scalability for high-dimensional gene expression analysis.

    Purpose of the Study:

    • To introduce Ensemble-Based Logical Binary QANA (LBQANA_En), an enhanced differential evolution algorithm for breast cancer biomarker detection.
    • To overcome limitations of small sample sizes and complex gene expression data through ensemble integration.

    Main Methods:

    • Developed LBQANA_En, inspired by quantum navigation and utilizing logical operators (XOR, OR).
    • Integrated six gene expression datasets to enhance robustness and handle data complexity.
    • Applied LBQANA_En to identify key breast cancer biomarkers.

    Related Experiment Videos

    Last Updated: May 13, 2026

    Quantitative Analysis of Cancer Metastasis using an Avian Embryo Model
    08:40

    Quantitative Analysis of Cancer Metastasis using an Avian Embryo Model

    Published on: May 30, 2011

    Main Results:

    • LBQANA_En demonstrated superior performance over other binary QANA variants in biomarker detection.
    • Identified key biomarkers: LPL, LEP, CD36, CDC20, TOP2A, and EZH2.
    • Achieved a high F1 score of 98.958%, significantly improving breast cancer detection accuracy and reducing false positives.

    Conclusions:

    • LBQANA_En offers a powerful new tool for large-scale global optimization in gene expression analysis.
    • The identified biomarkers provide insights into critical pathways like AMPK and PPAR signaling.
    • This research sets a new benchmark in computational biology, advancing diagnostic techniques for breast cancer.