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Updated: May 24, 2026

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Opt-DMA-trans: Explainable optimized residual dense-assisted multi-scale attention transformer assisted multi-class

Paritosh Kumar1, Akshay Deepak1, Prabhat Kumar1

  • 1Department of Computer Science & Engineering, National Institute of Technology, Patna, Bihar 800005, India.

Computational Biology and Chemistry
|May 22, 2026
PubMed
Summary

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This study introduces an Explainable AI model to accurately classify multi-class Nucleic Acid Binding Proteins (NABP). The novel Opt-DMA-Trans model achieves high accuracy, improving upon existing methods for protein sequence analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Artificial Intelligence in Genomics

Background:

  • Nucleic acid binding proteins (NABP), including DNA binding proteins (DBP) and RNA binding proteins (RBP), are crucial for gene regulation and disease pathways.
  • Experimental determination of NABP is time-consuming and costly, struggling to keep pace with vast protein sequence data.
  • Existing computational models often lack interpretability and struggle with complex relationships in protein sequences due to high-dimensional, redundant features.

Purpose of the Study:

  • To develop an interpretable and accurate Explainable AI (XAI) model for the multi-class classification of NABP.
  • To address limitations of existing models in handling complex protein sequence relationships and feature redundancy.
  • To enhance the efficiency and scalability of NABP identification using advanced machine learning techniques.
Keywords:
Local Interpretable agnostic ExplanationsMulti-scale attention transformerOrangutan optimization algorithmResidual networkShapely Additive explanations

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Main Methods:

  • Data sourced from the UniProtKB dataset with pre-processing to remove duplicates and irrelevant information.
  • Feature extraction using the Python ProtLearn library, followed by feature selection via Improved Orangutan Optimization Algorithm (IOOA) and Autoencoder (AE).
  • Classification performed using an optimized Residual Dense assisted Multi-Scale Attention Transformer (Opt-DMA-Trans) model, with Shapley Additive Explanations (SHAP) for interpretability.

Main Results:

  • The proposed Opt-DMA-Trans model achieved high performance metrics: 98.95% accuracy, 98.95% precision, and 98.75% F1-score.
  • Feature selection methods (IOOA and AE) effectively reduced redundancy and improved prediction.
  • The SHAP technique provided insights into the model's decision-making process, enhancing interpretability.

Conclusions:

  • The developed Explainable AI model demonstrates high efficacy and accuracy in predicting multi-class NABP.
  • The integration of advanced feature selection and attention-based transformer architecture offers a robust solution for NABP classification.
  • This approach provides a scalable, interpretable, and cost-effective alternative to experimental methods for identifying NABP.