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Updated: Aug 12, 2025

Dissection of Enhancer Function Using Multiplex CRISPR-based Enhancer Interference in Cell Lines

Dissection of Enhancer Function Using Multiplex CRISPR-based Enhancer Interference in Cell Lines

Published on: June 2, 2018

iEnhancer-SKNN: a stacking ensemble learning-based method for enhancer identification and classification using

Hao Wu^1,2, Mengdi Liu¹, Pengyu Zhang¹

¹College of Information Engineering, Northwest A&F University, Yangling, 712100, Shaanxi, China.

Briefings in Functional Genomics

|January 30, 2023

Summary

This summary is machine-generated.

This study introduces iEnhancer-SKNN, a novel stacking ensemble learning method for accurately identifying DNA enhancers and classifying them as strong or weak. The method improves prediction accuracy for both enhancer identification and classification tasks.

Keywords:

enhancer identification sequence analysis stacking ensemble learning transcription factor motifs

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AAV Deployment of Enhancer-Based Expression Constructs In Vivo in Mouse Brain

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Published on: March 31, 2022

Area of Science:

Genomics and Bioinformatics
Computational Biology
Molecular Biology

Background:

Enhancers are crucial distal cis-regulatory elements in gene regulation, located in non-coding DNA.
Identifying enhancers is challenging due to their dispersed nature, lack of specific sequence features, and distance from target promoters.

Purpose of the Study:

To develop an accurate stacking ensemble learning method for identifying enhancers from DNA sequence data.
To classify identified enhancers into strong and weak categories.

Main Methods:

A fusion feature matrix was created by combining Kmer, PseDNC, PCPseDNC, and Z-Curve9 features.
A two-layer stacking ensemble model, iEnhancer-SKNN, was constructed using five K-Nearest Neighbor (KNN) base models and a Logistic Regression meta-model.
The model performs enhancer identification (enhancer vs. non-enhancer) in the first layer and strength classification (strong vs. weak enhancer) in the second layer.

Main Results:

iEnhancer-SKNN achieved 81.75% accuracy in enhancer identification, outperforming existing predictors by 1.35% to 8.75%.
In enhancer classification, iEnhancer-SKNN reached 80.50% accuracy, surpassing other methods by 5.5% to 25.5%.
Key transcription factor binding site motifs within enhancer regions were identified, and their biological functions were explored.

Conclusions:

The iEnhancer-SKNN stacking ensemble method provides a robust and accurate approach for enhancer identification and strength classification.
The findings contribute to a better understanding of enhancer function and regulatory mechanisms in the non-coding genome.
The source code and data are publicly available for further research and application.