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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?

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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...

Chromatin Position Affects Gene Expression

Chromatin Position Affects Gene Expression

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Cell Specific Gene Expression

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

Cell Specific Gene Expression

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mRNA Stability and Gene Expression

mRNA Stability and Gene Expression

The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
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Related Experiment Video

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Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells

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Disease genes prediction by HMM based PU-learning using gene expression profiles.

Ozra Nikdelfaz¹, Saeed Jalili¹

¹Tarbiat Modares University, Computer Engineering Department, Islamic Republic of Iran.

Journal of Biomedical Informatics

|March 25, 2018

Summary

This summary is machine-generated.

This study introduces a novel machine learning method using hidden Markov models to predict disease candidate genes from gene expression profiles. The new approach significantly improves upon existing methods for identifying genes linked to diseases.

Keywords:

Disease gene prediction Gene expression profile Hidden Markov model Positive-unlabeled learning

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Identifying disease-associated genes is vital for biomedical research.
Genes linked to diseases with similar phenotypes often share functional properties.
Machine learning, including Positive-Unlabeled Learning (PU-Learning), is used for disease gene prediction.

Purpose of the Study:

To develop and evaluate a novel method for predicting disease candidate genes.
To leverage gene expression profiles and hidden Markov models for enhanced prediction accuracy.

Main Methods:

A novel method based on learning hidden Markov models is proposed.
The method utilizes gene expression profiles for disease gene prediction.
The approach was evaluated using 398 known disease genes and 12001 unlabeled genes.

Main Results:

The proposed method demonstrated significant improvement compared to existing literature methods.
Experimental results indicate enhanced accuracy in predicting disease candidate genes.
The hidden Markov model approach effectively utilizes gene expression data.

Conclusions:

The novel hidden Markov model-based method offers a significant advancement in disease gene prediction.
Gene expression profiles are valuable data for identifying novel disease-associated genes.
This method holds promise for accelerating biomedical research by improving gene discovery.