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Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome. Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...

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Updated: Aug 29, 2025

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation

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A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns.

Ahmad Hassan Butt¹, Tamim Alkhalifah², Fahad Alturise³

¹Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan.

Scientific Reports

|September 7, 2022

Summary

This study introduces a new method to identify gene enhancers and predict their strength using DNA sequence features. The approach improves prediction accuracy, aiding in gene expression regulation research.

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Published on: April 29, 2020

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Enhancers are crucial regulatory DNA elements controlling gene expression by influencing RNA and protein synthesis.
Predicting enhancers is challenging due to their distance from target genes, lack of conserved motifs, and cell-specific activity.
Existing bioinformatics tools require improved prediction accuracy and practical applicability.

Purpose of the Study:

To develop a novel computational method for identifying enhancer regions and predicting their regulatory strength.
To enhance the accuracy and practical value of enhancer prediction in DNA sequences.
To utilize nucleotide composition and statistical moment-based features for improved enhancer identification.

Main Methods:

A new method was developed utilizing nucleotide composition and statistical moment-based features.
The method was evaluated using fivefold and tenfold cross-validation.
Performance was compared against state-of-the-art techniques.

Main Results:

The proposed method achieved 86.5% accuracy in predicting enhancer sites.
The method achieved 72.3% accuracy in predicting enhancer strength.
The study demonstrated superior performance compared to existing methods.

Conclusions:

Statistical moment-based features offer potential for efficient and successful enhancer identification and strength evaluation.
The developed method shows promise for advancing the understanding and prediction of gene regulatory elements.
Source code is available for community use to facilitate further research.