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Cis-regulatory Sequences

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Related Experiment Video

Updated: Jan 7, 2026

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

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Published on: February 18, 2022

iPro2L-Kresidual: A High-Performance Promoter Identification Model for Sequence Nonlinearity and Context Mining.

Yanjuan Li¹, Shicai Li², Guojun Sheng²

¹College of Electrical and Information Engineering, Quzhou University, Quzhou 324000, China.

|December 30, 2025

Summary

This summary is machine-generated.

This study introduces iPro2L-Kresidual, a novel model for promoter identification. It achieves high accuracy in predicting promoter sites and their strength, improving upon existing methods.

Keywords:

DNA promoter bioinformatics deep learning sequence analysis two-stage prediction

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Last Updated: Jan 7, 2026

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

Published on: June 28, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Promoters are crucial non-coding DNA sequences regulating gene expression.
Abnormalities in promoters are linked to diseases like cancer, diabetes, and heart disease.
Existing models struggle with nonlinear feature extraction and context relationship capture, limiting promoter identification performance.

Purpose of the Study:

To develop an advanced model for accurate promoter identification.
To enhance the extraction of nonlinear features and sequence context relationships.
To improve the classification performance of promoter identification models.

Main Methods:

Proposed iPro2L-Kresidual model integrating residual structure and KAN network (Kresidual module).
Enhanced Transformer encoder using gated recurrent units for local and global context feature extraction.
Implemented a regularized label smoothing cross-entropy loss function for training stability.

Main Results:

Achieved 94.28% accuracy for promoter identification and 90.55% for promoter strength identification via 5-fold cross-validation.
Demonstrated strong generalization ability with 93.13% prediction accuracy on an independent dataset.
The Kresidual module improved nonlinear sequence feature expression, and enhanced Transformer captured sequence context effectively.

Conclusions:

iPro2L-Kresidual offers a novel and effective approach for promoter site prediction.
The model overcomes limitations of existing methods in nonlinear feature extraction and context understanding.
This work provides a significant advancement in computational genomics for disease-related gene regulation studies.