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Related Experiment Video

Updated: Mar 21, 2026

Folding and Characterization of a Bio-responsive Robot from DNA Origami

Folding and Characterization of a Bio-responsive Robot from DNA Origami

Published on: December 3, 2015

DeepIM: Integrating Channel-Spatial Attention with Transformer for DNA i-Motif Folding Status Prediction.

Rui Wu^1,2, Hui Zhang¹, Li-Rong Zhang³

¹School of Electronic Information Engineering, Inner Mongolia University, Hohhot, Inner Mongolia 010021, China.

Journal of Chemical Information and Modeling

|March 19, 2026

Summary

DeepIM, a new deep learning model, accurately predicts i-motif (iM) DNA folding. This advancement aids in understanding gene regulation and cancer development by analyzing complex DNA structures.

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Area of Science:

Genomics and Bioinformatics
Computational Biology
Molecular Biology

Background:

i-Motifs (iM) are crucial DNA structures involved in gene regulation, telomere stability, and cancer.
Experimental detection methods for iMs are costly and low-throughput.
Existing computational models fail to capture intricate DNA sequence-structure relationships for iM formation.

Purpose of the Study:

To develop a novel deep learning model, DeepIM, for accurate and interpretable prediction of i-motif folding status.
To overcome limitations of traditional experimental and computational methods in iM detection.
To enhance understanding of sequence-structure relationships in DNA secondary structures.

Main Methods:

DeepIM utilizes a Transformer architecture with a channel-spatial attention (CSA) mechanism.
DNA sequences are encoded into k-mers with embedding and positional encoding.
CSA mechanism extracts local features (C-tracts, flanking regions) and Transformer models long-range dependencies.

Main Results:

DeepIM achieved 92.6% accuracy in predicting iM folding status, surpassing XGBoost (86.0%), random forest (87.0%), and iM-Seeker (90.3%).
The model demonstrated strong cross-cell-line generalization capabilities.
Attention weight analysis identified distinctive iM sequence patterns, validating model interpretability.

Conclusions:

DeepIM represents a significant advancement in DNA secondary structure prediction using deep learning.
The model offers a highly accurate and interpretable method for analyzing i-motif formation.
DeepIM facilitates a deeper understanding of the complex sequence-structure interplay in DNA, with implications for gene regulation and disease research.