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

Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

You might also read

Related Articles

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

Sort by

Same author

Isolation and identification of hasubanan alkaloids having anti-cholinesterase and antioxidant activity from the stem Stephania japonica.

BMC complementary medicine and therapies·2026

Same author

In-silico identification of bacterial key-genes directly or indirectly associated with the development and progression of colorectal cancer for exploring anti-bacterial agents.

PloS one·2026

Same author

Large-scale transcriptomic data mining using explainable XGBoost and SHAP reveals shared biomarkers and molecular mechanisms between type-2 diabetes and triple-negative breast cancer for drug repurposing.

BioData mining·2026

Same author

ACL Reconstruction With Attachment-Sparing Hamstring Autograft Results in Earlier Graft Maturation and Better Short-Term Clinical Outcome in Comparison to Free Graft: A Randomized Controlled Trial.

The American journal of sports medicine·2026

Same author

Genome-wide identification and characterization of the RNAi gene families in Brassica rapa L. highlighting their regulatory components and underlying functions involving crop improvement.

BMC genomic data·2026

Same author

Efficacy of Diode Laser Therapy in Managing Recurrent Aphthous Stomatitis and Oral Lichen Planus: A Systematic Review.

Photobiomodulation, photomedicine, and laser surgery·2026

Search research articles

Related Experiment Video

Updated: Feb 18, 2026

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

Robust Feature Selection Approach for Patient Classification using Gene Expression Data.

Md Shahjaman^1,2, Nishith Kumar^1,3, Md Shakil Ahmed¹

¹Bioinformatics Lab, Department of Statistics, University of Rajshahi-6205, Bangladesh.

|November 23, 2017

Summary

Robust SAM improves patient classification accuracy using gene expression data. This method enhances feature selection, outperforming traditional t-tests and SAM, especially with small sample sizes and outliers.

Keywords:

Feature selection classification robust SAM β-divergence estimators

More Related Videos

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

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Related Experiment Videos

Last Updated: Feb 18, 2026

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

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

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Feature selection (FS) from gene expression data (GED) is crucial for patient classification.
Classical t-tests and Significance Analysis of Microarrays (SAM) have limitations with small sample sizes and outliers in GED analysis.
Robust SAM, utilizing minimum β-divergence estimators, addresses these limitations for identifying differentially expressed (DE) genes.

Purpose of the Study:

To evaluate the efficacy of robust SAM as a feature selection method for patient classification using GED.
To compare the performance of robust SAM against classical t-test and SAM when integrated with common classifiers.
To identify novel DE genes and their associated pathways in cancer using robust SAM.

Main Methods:

Employed robust SAM for feature selection in conjunction with four classifiers: Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Naive Bayes.
Validated the approach using both simulated and real gene expression datasets.
Conducted KEGG pathway enrichment analysis on newly identified DE genes.

Main Results:

Robust SAM demonstrated improved performance of the four classifiers compared to classical t-test and SAM across simulated and real datasets.
The study identified 21 additional DE genes from a real Colon cancer dataset using robust SAM, which were missed by classical t-test and SAM.
KEGG pathway enrichment analysis revealed that these 21 genes are implicated in significant cancer-related pathways.

Conclusions:

Robust SAM is a superior feature selection technique for patient classification with gene expression data, particularly in challenging scenarios like small sample sizes or data with outliers.
The application of robust SAM can lead to the discovery of novel biomarkers and pathways relevant to diseases such as cancer.
This approach enhances the accuracy and reliability of patient classification models built on gene expression data.