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Related Concept Videos

Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

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Related Experiment Video

Updated: May 19, 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

omniBiomarker: A Web-Based Application for Knowledge-Driven Biomarker Identification.

John H Phan, Andrew N Young, May D Wang

    IEEE Transactions on Bio-Medical Engineering
    |August 16, 2012
    PubMed
    Summary
    This summary is machine-generated.

    We developed omniBiomarker, a tool to select algorithms for identifying cancer biomarkers from genomic data. This knowledge-driven approach enhances biomarker discovery and improves clinical prediction accuracy.

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    Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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    Published on: May 17, 2019

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    Last Updated: May 19, 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

    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

    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Biomarker identification from high-throughput genomic data is challenging due to data characteristics (small sample size, large feature size) and numerous available algorithms.
    • Algorithm selection for biomarker discovery often lacks biological relevance, leading to unstable results and poor reproducibility.

    Purpose of the Study:

    • To introduce omniBiomarker, a web-based application for guiding the selection of biologically relevant algorithms for biomarker identification.
    • To leverage the NCI Cancer Gene Index and a curated knowledge base of cancer biomarkers to enhance algorithm selection.

    Main Methods:

    • Developed a method to compute the biological relevance of feature selection algorithms.
    • Integrated this method into the omniBiomarker web application, utilizing the NCI Cancer Gene Index.
    • Validated the approach using externally validated knowledge bases of cancer biomarkers.

    Main Results:

    • The knowledge-driven approach for biomarker identification demonstrated potential to improve clinical prediction performance.
    • Results suggest that selecting algorithms based on biological relevance enhances the reliability of biomarker discovery.

    Conclusions:

    • omniBiomarker provides a framework for selecting appropriate algorithms, addressing the challenge of biomarker discovery from genomic data.
    • This approach can improve the stability and reproducibility of biomarker identification, ultimately aiding clinical prediction.