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...

Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...

You might also read

Related Articles

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

Sort by

Same author

Evaluating feature extraction in ovarian cancer cell line co-cultures using deep neural networks.

Communications biology·2025

Same author

Transcriptomic analyses reveal three distinct molecular subgroups of Merkel cell carcinoma with differing prognoses.

International journal of cancer·2023

Same author

Integrative multi-omics and drug response profiling of childhood acute lymphoblastic leukemia cell lines.

Nature communications·2022

Same author

Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis.

Bioinformatics (Oxford, England)·2014

Same author

Break-induced replication repair of damaged forks induces genomic duplications in human cells.

Science (New York, N.Y.)·2013

Same author

Comprehensive data-driven analysis of the impact of chemoinformatic structure on the genome-wide biological response profiles of cancer cells to 1159 drugs.

BMC bioinformatics·2012

Same journal

Interpretable machine learning for Parkinson's disease diagnosis, staging, and biological mechanism exploration: a multicenter analysis.

BioData mining·2026

Same journal

Learning a distance for the clustering of patients with amyotrophic lateral sclerosis.

BioData mining·2026

Same journal

Multi-domain feature fusion with variational mode decomposition and hybrid LightGBM-Logistic Regression for multi-class seizure classification.

BioData mining·2026

Same journal

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 journal

AVSeg-XAI: Deep learning framework for A/V segmentation with vascular features reveals retinal oculomics as biomarker for cardiovascular disease.

BioData mining·2026

Same journal

Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.

BioData mining·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 3, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Alignment of gene expression profiles from test samples against a reference database: New method for context-specific

Sami K Kilpinen¹, Kalle A Ojala, Olli P Kallioniemi

¹Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Tukholmankatu 8, Helsinki, Finland. sami.k.kilpinen@helsinki.fi.

|April 2, 2011

Summary

This summary is machine-generated.

We developed Alignment of Microarray Gene Expression Profiles (AGEP) to interpret gene expression data. This method accurately identifies tissue origins and characterizes disease-specific gene expression changes, facilitating high-throughput data analysis.

More Related Videos

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Related Experiment Videos

Last Updated: Jun 3, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Gene expression microarray data are publicly available but underutilized for individual sample analysis due to data heterogeneity.
Current methods for interpreting individual microarray samples against large reference datasets are not well-developed.

Purpose of the Study:

To develop a rapid and powerful approach for aligning microarray gene expression profiles (AGEP) from test samples with a large annotated public reference database.
To demonstrate how AGEP can facilitate the interpretation of individual microarray data.

Main Methods:

AGEP calculates kernel density distributions for gene expression levels in reference tissues.
It quantifies similarity between test samples and reference tissues, identifying typical and atypical genes.
A reference database of 1654 normal tissue samples from 44 tissue types was utilized.

Main Results:

Leave-one-out validation showed AGEP correctly identified the tissue of origin for 93.6% of samples.
Independent validation achieved 87% accuracy for exact tissue type and 97% for related tissue types.
AGEP analysis of Duchenne muscular dystrophy (DMD) samples provided quantitative insights into pathogenesis and identified differentially expressed genes like SAMD4A.

Conclusions:

AGEP is a widely applicable method for rapid and comprehensive interpretation of microarray data.
It enables the definition of tissue- and disease-specific gene expression changes and characterization of cellular differentiation.
This quantitative comparison against large-scale annotated databases offers a paradigm for analyzing high-throughput data.