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

DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

You might also read

Related Articles

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

Sort by

Same author

Integrative gene expression and heterologous functional analysis identify candidate regulators of apomixis in <i>Eragrostis curvula</i>.

Frontiers in plant science·2026

Same author

A Multimodal Sequence-to-Sequence Model for Automatic Assignment of ATC Codes in Drug Discovery and Repurposing.

Journal of chemical information and modeling·2026

Same author

Bacterial diversity using metagenomics of 16s rDNA in water kefir, an innovative source of probiotics for bee nutrition.

Revista Argentina de microbiologia·2024

Same author

Papillary Thyroid Carcinoma: A thorough Bioinformatic Analysis of Gene Expression and Clinical Data.

Genes·2023

Same author

Design of New Dispersants Using Machine Learning and Visual Analytics.

Polymers·2023

Same author

Hemoxygenase-1 Promotes Head and Neck Cancer Cell Viability.

Antioxidants (Basel, Switzerland)·2022

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: Jun 2, 2026

Profiling of Pre-micro RNAs and microRNAs using Quantitative Real-time PCR (qPCR) Arrays

Profiling of Pre-micro RNAs and microRNAs using Quantitative Real-time PCR (qPCR) Arrays

Published on: December 3, 2010

Discovering time-lagged rules from microarray data using gene profile classifiers.

Cristian A Gallo¹, Jessica A Carballido, Ignacio Ponzoni

¹Laboratorio de Investigación y Desarrollo en Computación Científica (LIDeCC), Departamento de Ciencias e Ingeniería de la Computación, Universidad Nacional del Sur, Av, Alem 1253, 8000, Bahía Blanca, Argentina.

BMC Bioinformatics

|April 29, 2011

Summary

This summary is machine-generated.

A new algorithm, GRNCOP2, reconstructs time-delayed gene regulatory networks from genome-wide data. This model-free approach accurately infers gene relationships and time-trends, outperforming existing methods.

More Related Videos

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

Published on: January 7, 2019

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Related Experiment Videos

Last Updated: Jun 2, 2026

Profiling of Pre-micro RNAs and microRNAs using Quantitative Real-time PCR (qPCR) Arrays

Profiling of Pre-micro RNAs and microRNAs using Quantitative Real-time PCR (qPCR) Arrays

Published on: December 3, 2010

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

Published on: January 7, 2019

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Area of Science:

Systems Biology
Bioinformatics
Computational Biology

Background:

Gene regulatory networks are fundamental to all life processes.
Genome-wide time series data enables the discovery of time-delayed gene regulatory networks.
Understanding these networks is crucial for deciphering molecular mechanisms.

Purpose of the Study:

To develop a novel algorithm for reconstructing time-delayed gene regulatory networks.
To infer gene regulatory relationships from multiple genome-wide time series datasets.
To validate the algorithm's performance against existing methods.

Main Methods:

Development of GRNCOP2 (Gene Regulatory Network inference by Combinatorial OPtimization 2), a model-free algorithm.
Utilizing combinatorial optimization of gene profile classifiers.
Application to yeast cell-cycle time series data and multiple public datasets.

Main Results:

GRNCOP2 accurately infers time-delayed gene regulatory relationships across various time spans.
The algorithm outperforms existing methods in reconstructing gene regulatory networks.
Validation on yeast cell-cycle data and genome-wide studies shows high accuracy and scalability.

Conclusions:

GRNCOP2 is a novel, model-free method for inferring time-delayed gene regulatory networks.
The algorithm effectively predicts meaningful gene associations and their time-trends.
Validated on multiple datasets, it offers a robust approach for biological network inference.