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

You might also read

Related Articles

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

Sort by
Same author

Learning Where to Look: Differentiable Slice Selection and Efficient Channel Attention for FCD-II MRI Classification.

IEEE journal of biomedical and health informatics·2026
Same author

$n$-Cylindrical Symbolic Response, a Standalone and Synergistic Biomarker for Epilepsy Diagnosis on EEG Modality.

IEEE journal of biomedical and health informatics·2025
Same author

Radiotranscriptomics of non-small cell lung carcinoma for assessing high-level clinical outcomes using a machine learning-derived multi-modal signature.

Biomedical engineering online·2023
Same author

Design of a Multi-Feature Classification Scheme for Infant Epileptic Seizures.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2023
Same author

EEG Source Analysis with a Convolutional Neural Network and Finite Element Analysis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2023
Same author

Dermal features derived from optoacoustic tomograms via machine learning correlate microangiopathy phenotypes with diabetes stage.

Nature biomedical engineering·2023
Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024
See all related articles

Related Experiment Video

Updated: Mar 11, 2026

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance
04:58

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

4.4K

Introducing a Stable Bootstrap Validation Framework for Reliable Genomic Signature Extraction.

Nikolaos-Kosmas Chlis, Ekaterini S Bei, Michalis Zervakis

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |December 4, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new machine learning framework to identify stable genomic signatures for disease research. The method ensures reliable candidate gene lists, improving the validity of genotype-phenotype association studies.

    More Related Videos

    Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example
    05:45

    Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example

    Published on: March 11, 2020

    9.4K
    Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
    14:06

    Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

    Published on: June 23, 2012

    15.8K

    Related Experiment Videos

    Last Updated: Mar 11, 2026

    Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance
    04:58

    Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

    Published on: December 13, 2024

    4.4K
    Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example
    05:45

    Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example

    Published on: March 11, 2020

    9.4K
    Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
    14:06

    Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

    Published on: June 23, 2012

    15.8K

    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Identifying candidate genes for phenotypes like cancer is crucial but challenging.
    • Current machine learning methods for genomic signatures lack stability, impacting research reliability.
    • Instability in gene lists arises from minor variations in training data.

    Purpose of the Study:

    • To develop a robust framework for extracting stable and reliable genomic signatures.
    • To address the limitations of traditional methods in generating consistent candidate gene lists.
    • To enhance the validity and reliability of genotype-phenotype association studies.

    Main Methods:

    • A novel framework for candidate gene extraction is proposed.
    • The methodology enforces result stability during the validation phase.
    • Statistical tests are employed to assess the significance and consistency of extracted signatures.

    Main Results:

    • The proposed framework yields stable and reliable lists of candidate genes.
    • The method's stability is independent of specific feature selection or classification algorithms.
    • Independent executions demonstrated high consistency of the extracted genomic signatures.

    Conclusions:

    • Stability is a critical factor in genomic signatures, complementing prediction accuracy.
    • The developed framework enhances the trustworthiness of bioinformatics research.
    • This approach facilitates more reliable discovery of genotype-phenotype causal links.