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Related Experiment Video

Updated: May 8, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
08:20

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

Harnessing Terminal Signal-Aware Deep Learning for Accurate Multi-Class Secreted Effector Identification.

Lesong Wei, Shida He, Quan Zou

    IEEE Journal of Biomedical and Health Informatics
    |May 6, 2026
    PubMed
    Summary

    This study introduces TermSE, a new deep learning framework that improves the identification of bacterial secreted effectors by focusing on terminal sequence signals. TermSE enhances accuracy and interpretability in predicting these crucial proteins for understanding bacterial pathogenesis.

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    Area of Science:

    • Microbiology
    • Bioinformatics
    • Computational Biology

    Background:

    • Gram-negative bacterial secreted effectors are vital for pathogenesis, requiring accurate identification.
    • Current deep learning methods often overlook terminal regions containing critical secretion signals.
    • Class imbalance among effector types poses a significant challenge for prediction models.

    Purpose of the Study:

    • To develop a novel framework, TermSE, for accurate multi-class identification of bacterial secreted effectors.
    • To incorporate terminal sequence features alongside global representations for enhanced characterization.
    • To address the challenge of class imbalance in secreted effector prediction.

    Main Methods:

    • TermSE utilizes convolutional neural networks on protein language model embeddings to capture N-terminal and C-terminal features.

    Related Experiment Videos

    Last Updated: May 8, 2026

    Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
    08:20

    Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

    Published on: October 27, 2023

  • Global sequence representations are integrated for multi-view sequence characterization.
  • A cosine-normalized classifier with weighted sampling is employed to handle class imbalance.

    • Main Results:

    • TermSE significantly outperforms existing methods in both cross-validation and independent testing.
    • The framework demonstrates robust generalization across varying protein sequence identities.
    • Interpretability analysis confirms TermSE focuses on biologically relevant terminal patterns.

    Conclusions:

    • TermSE offers an effective and interpretable solution for secreted effector discovery.
    • The terminal signal-aware approach advances the field of bacterial pathogenesis research.
    • This method holds promise for identifying novel secreted effectors and understanding their functions.