Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Combination Therapies and Personalized Medicine

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...

You might also read

Related Articles

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

Sort by

Same author

Comprehensive review and assessment of multi-species splicing variant prediction: task-specific deep learning models and genomic foundation models.

Briefings in bioinformatics·2026

Same author

Safety and Effectiveness of an Integrative Treatment of Acupuncture-Based Intervention in Survivors of Breast Cancer With Postmastectomy Pain Syndrome: Protocol for a Single-Center, Single-Arm Exploratory Trial.

JMIR research protocols·2026

Same author

Genetic contributors to postoperative delirium and their implications for dementia outcomes.

Anesthesiology·2026

Same author

Corrigendum to "Identification of a novel antimicrobial peptide Gp-AMP1 with broad-spectrum and exceptional stability from deep-sea mussel Gigantidas platifrons" [Food Chem. 501 (2026) 147576].

Food chemistry·2026

Same author

Olanzapine Prophylaxis for Opioid-Induced Nausea and Vomiting: A Single-Arm Exploratory Study.

Journal of pain and symptom management·2026

Same author

EvoRMD: integrating biological context and evolutionary RNA language models for interpretable prediction of RNA modifications.

Genome biology·2026

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

Search research articles

Related Experiment Video

Updated: Aug 3, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Genetic algorithm-based feature selection with manifold learning for cancer classification using microarray data.

Zixuan Wang¹, Yi Zhou², Tatsuya Takagi³

¹Division of Medical Data Informatics, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan. zixuan-wang@ims.u-tokyo.ac.jp.

BMC Bioinformatics

|April 8, 2023

Summary

This summary is machine-generated.

This study introduces Iso-GA, a novel gene selection method for cancer classification using microarray data. Iso-GA effectively identifies critical genes, improving classification accuracy with fewer selections.

Keywords:

Cancer classification Gene selection Genetic algorithm Manifold algorithm Microarray data

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Related Experiment Videos

Last Updated: Aug 3, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Microarray data is crucial for cancer classification but presents challenges due to high dimensionality (large p, small n).
Effective gene selection is vital for accurate cancer classification from complex microarray datasets.

Purpose of the Study:

To develop a novel gene selection method for cancer classification using microarray data.
To address the limitations of existing methods in handling the "large p, small n" characteristic of gene expression data.

Main Methods:

Proposed Iso-GA, a hybrid method combining Isomap (manifold learning) with Genetic Algorithm (GA).
Utilized Davies-Bouldin index for evaluating candidate solutions and a probability-based framework to refine gene selection.
Evaluated performance on eight benchmark cancer microarray datasets.

Main Results:

Iso-GA demonstrated superior performance compared to other gene selection methods.
Achieved high classification accuracy with a reduced number of selected genes.
Effectively captured latent nonlinear structures in gene expression data.

Conclusions:

The Iso-GA method is effective in selecting a minimal set of critical genes from microarray data.
Achieves competitive cancer classification performance, highlighting its utility in bioinformatics.