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

Quantifying and Rejecting Outliers: The Grubbs Test

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...

You might also read

Related Articles

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

Sort by

Same author

Editorial: Bioresorbable materials: What is new?

Frontiers in bioengineering and biotechnology·2026

Same author

Apoptosis execution is spatially regulated.

Apoptosis : an international journal on programmed cell death·2026

Same author

Machine Learning-Based Frailty Prediction and Classification in Community-Dwelling Older Adults: A Systematic Review of Validation, Explainability, and Implementation Readiness.

Healthcare (Basel, Switzerland)·2026

Same author

GeoFusion-3D: Multi-Scale Geomorphic Feature Fusion for Landslide Scar Detection Using UAV-Mounted LiDAR.

Sensors (Basel, Switzerland)·2026

Same author

Differences in clinical presentation between heart failure patients with reduced and preserved ejection fraction - analysis of the INTELHEART database.

Acta cardiologica·2026

Same author

Ionic Extracts of Magnesium Powders Promote In Vitro Lymphangiogenesis.

Biomedicines·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: Mar 9, 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

Minimum redundancy maximum relevance feature selection approach for temporal gene expression data.

Milos Radovic^1,2, Mohamed Ghalwash^3,4,5, Nenad Filipovic^6,7

¹Center for Data Analytics and Biomedical Informatics, College of Science and Technology, Temple University, North 12th Street, Philadelphia, 19122, PA, USA. milos.radovic@temple.edu.

BMC Bioinformatics

|January 5, 2017

Summary

This summary is machine-generated.

We developed a novel temporal feature selection method for gene expression data. This approach effectively handles time-series data, improving accuracy by selecting more discriminative features.

Keywords:

Feature selection Gene expression Temporal data

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

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Related Experiment Videos

Last Updated: Mar 9, 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

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Area of Science:

Machine Learning
Bioinformatics
Genomics

Background:

Feature selection is crucial for machine learning, especially in gene expression studies with temporal data.
Traditional methods often fail with time-series data due to information loss from flattening.
A new approach is needed to preserve temporal dynamics in feature selection.

Purpose of the Study:

To propose a new feature selection method for multivariate temporal gene expression data.
To address the limitations of existing methods in handling time-series biological data.
To develop a method that retains temporal information without data flattening.

Main Methods:

Developed a temporal minimum redundancy - maximum relevance (TMRMR) feature selection approach.
Calculated gene relevance by averaging F-statistic values across time steps.
Measured gene redundancy using a dynamical time warping approach.

Main Results:

Evaluated the TMRMR method on three human viral challenge gene expression datasets.
The proposed method significantly outperformed existing alternatives in accuracy.
Achieved accuracy improvements in 34 out of 54 experiments, compared to a maximum of 4 for others.

Conclusions:

Introduced a filter-based feature selection method for temporal gene expression data.
The TMRMR method effectively integrates temporal information using F-statistics and dynamical time warping.
Incorporating temporal dynamics enhances the selection of more discriminative features.