Search research articles

Related Concept Videos

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...

Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

What is Gene Expression?

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...

What is Gene Expression?

What is Gene Expression?

A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then processed and...

Regulation of Expression at Multiple Steps

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...

You might also read

Related Articles

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

Sort by

Same author

Gene Expression Profiles of CD133-positive Fractions Predict the Survival of Individuals with Acute Myeloid Leukemia.

Cancer genomics & proteomics·2019

Same author

RATE OF ENVIRONMENTAL CHANGE AND ADAPTATION IN THE COPEPOD EURYTEMORA AFFINIS.

Evolution; international journal of organic evolution·2017

Same author

Expression at a 20L scale and purification of the extracellular domain of the Schistosoma mansoni TSP-2 recombinant protein: a vaccine candidate for human intestinal schistosomiasis.

Human vaccines & immunotherapeutics·2013

Same author

Major histocompatibility complex class II+ invariant chain negative breast cancer cells present unique peptides that activate tumor-specific T cells from breast cancer patients.

Molecular & cellular proteomics : MCP·2012

Same author

A reexamination of information theory-based methods for DNA-binding site identification.

BMC bioinformatics·2009

Same author

Chlorfenapyr and mallard ducks: overview, study design, macroscopic effects, and analytical chemistry.

Environmental toxicology and chemistry·2006

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

Search research articles

Related Experiment Video

Updated: Jun 23, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Protein expression profiling.

Brian P Bradley¹, Bose Kalampanayil, Michael C O'Neill

¹Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA.

Methods in Molecular Biology (Clifton, N.J.)

|April 22, 2009

Summary

This study introduces a machine learning method to identify protein expression signatures (PES) for accurate disease classification. The approach uses artificial neural networks (ANN) to analyze protein data, achieving high diagnostic accuracy with minimal misclassifications.

More Related Videos

High-throughput Protein Expression Generator Using a Microfluidic Platform

High-throughput Protein Expression Generator Using a Microfluidic Platform

Published on: August 23, 2012

Polysome Profiling without Gradient Makers or Fractionation Systems

Polysome Profiling without Gradient Makers or Fractionation Systems

Published on: June 1, 2021

Related Experiment Videos

Last Updated: Jun 23, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

High-throughput Protein Expression Generator Using a Microfluidic Platform

High-throughput Protein Expression Generator Using a Microfluidic Platform

Published on: August 23, 2012

Polysome Profiling without Gradient Makers or Fractionation Systems

Polysome Profiling without Gradient Makers or Fractionation Systems

Published on: June 1, 2021

Area of Science:

Proteomics and Bioinformatics
Biotechnology and Drug Discovery

Background:

Protein expression profiling identifies proteins in specific conditions for drug discovery and diagnosis.
Protein expression signatures (PES) are crucial for understanding disease and stress responses.
Current methods require protein identification before classification, which can be inefficient.

Purpose of the Study:

To present a machine learning-based method for isolating protein expression signatures (PES) that accurately classify treatment groups.
To demonstrate the utility of artificial neural networks (ANN) in identifying diagnostic protein markers before their specific identification.
To establish a method for accurate diagnosis and classification based on protein expression data.

Main Methods:

Utilized machine learning, specifically artificial neural networks (ANN), for protein expression analysis.
Converted protein expression data from multiple two-dimensional electrophoresis (2DE) gel patterns into digital formats for ANN input.
Trained ANN models on a subset of digitized gel images and validated their performance on a separate test set.

Main Results:

The developed ANN models accurately classified treatment classes with 5% or less misclassification.
The method successfully identified significant protein variables that discriminate among different classes.
Demonstrated that isolated protein sets can effectively classify unknown samples, confirming their diagnostic potential.

Conclusions:

The machine learning approach, particularly ANN, is a powerful tool for isolating and identifying diagnostic protein expression signatures (PES).
This method complements traditional image analysis in proteomics, enabling more efficient multivariable statistical analyses.
The findings support the use of ANN for accurate disease diagnosis and classification based on protein expression profiles.