Jove
Visualize
Contact Us

Related Concept Videos

RNA-seq03:21

RNA-seq

12.2K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
12.2K
DNA Microarrays02:34

DNA Microarrays

21.4K
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...
21.4K
Ribosome Profiling02:24

Ribosome Profiling

4.2K
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...
4.2K

You might also read

Related Articles

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

Sort by
Same author

Decoding the reproductive microbiome: enabling clinical and biological insights through machine and deep learning.

Frontiers in endocrinology·2026
Same author

Automated Lesion Segmentation in Medical Imaging via Integration of nnU-Net Optimization and SAM Approach.

Biomedical engineering and computational biology·2026
Same author

Transforming psychological practice with precision mental health: introduction to the NOVA project.

Frontiers in psychology·2026
Same author

Interrelational Proteomic Sequence Features Enhance Predictive Modeling: Application to COVID-19 Severity.

Biomedicines·2026
Same author

Integrated Transcriptomic and Histological Analysis of TP53/CTNNB1 Mutations and Microvascular Invasion in Hepatocellular Carcinoma.

Genes·2026
Same author

Adjuvant Chemotherapy in Children With Enucleated Retinoblastoma and Histopathologic High-Risk Features: Survival Outcomes From a Single Institution in a Middle-Income Country.

Cureus·2026
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Feb 18, 2026

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells
16:24

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells

Published on: February 21, 2014

20.7K

Integration of RNA-Seq data with heterogeneous microarray data for breast cancer profiling.

Daniel Castillo1, Juan Manuel Gálvez2, Luis Javier Herrera2

  • 1Department of Computer Architecture and Technology, University of Granada, Periodista Rafael Gómez Montero, 2, Granada, 18014, Spain. cased@ugr.es.

BMC Bioinformatics
|November 22, 2017
PubMed
Summary

This study integrates microarray and RNA-Seq data to identify breast cancer biomarkers. A six-gene signature was developed, enabling accurate classification of unseen samples for improved cancer diagnosis.

Keywords:
Breast cancerCancerClassificationGene expressionIntegrationMicroarrayRNA-SeqRandom ForestSVMk-NN

More Related Videos

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer
10:36

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer

Published on: March 17, 2016

11.0K
Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
11:12

Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material

Published on: August 1, 2018

8.5K

Related Experiment Videos

Last Updated: Feb 18, 2026

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells
16:24

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells

Published on: February 21, 2014

20.7K
Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer
10:36

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer

Published on: March 17, 2016

11.0K
Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
11:12

Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material

Published on: August 1, 2018

8.5K

Area of Science:

  • Genomics
  • Bioinformatics
  • Biotechnology

Background:

  • Microarray gene expression datasets are publicly available but less accurate than RNA-Seq.
  • Integrating microarray and RNA-Seq data offers robust analysis despite technological differences.
  • RNA-Seq data acquisition is time-consuming and computationally intensive.

Purpose of the Study:

  • To develop a model for identifying breast cancer cell line gene signatures by integrating heterogeneous microarray and RNA-Seq data.
  • To enhance statistical significance through data integration.
  • To validate the robustness of identified Differentially Expressed Genes using a classification method for unseen data.

Main Methods:

  • Data integration of heterogeneous microarray and RNA-Seq datasets.
  • Identification of significant genes through the intersection of gene sets from individual and integrated data.
  • Development and validation of a classification method using training and testing datasets.
  • Application of a feature selection process to identify a minimal gene subset.

Main Results:

  • The data integration approach enabled analysis of gene expression samples from different technologies.
  • Intersection of gene sets revealed 98 potential technology-independent biomarkers.
  • Classification tasks achieved high accuracies, validating the identified gene set.
  • A final subset of six genes was selected for breast cancer diagnosis.

Conclusions:

  • A novel data integration method combining microarray and RNA-Seq data was introduced.
  • A six-gene subset, identified via feature selection, successfully classified available samples.
  • A new classification and diagnosis tool was developed and validated on unseen samples.