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Related Experiment Video

Updated: May 14, 2026

Remote Neuronal Activation Coupled with Automated Blood Sampling to Induce and Measure Circulating Luteinizing Hormone in Mice
08:56

Remote Neuronal Activation Coupled with Automated Blood Sampling to Induce and Measure Circulating Luteinizing Hormone in Mice

Published on: August 25, 2023

Attention-enhanced CNN-BiLSTM models for predicting luteinizing hormone sequence variations.

D Kalyani1, Vimala Devi1, A M Arunnagiri2

  • 1Department of Biomedical Engineering, College of Engineering, Guindy, Anna University, CEG, Chennai, Tamil Nadu, India.

Computational Biology and Chemistry
|May 12, 2026
PubMed
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This study introduces a deep learning framework to predict luteinizing hormone (LH) sequence variations. The novel CNN+Attention+BiLSTM model achieved 99.42% accuracy, enhancing reproductive health diagnostics.

Area of Science:

  • Biotechnology
  • Computational Biology
  • Genomics

Background:

  • Luteinizing hormone (LH) is critical for reproductive function.
  • Alterations in LH amino acid sequences can impact hormonal activity and lead to reproductive disorders.
  • Understanding protein sequence variations is key to diagnosing hormonal imbalances.

Purpose of the Study:

  • To develop a deep learning computational framework for predicting LH sequence alterations.
  • To compare the performance of various deep learning models, including CNN, BiLSTM, and hybrid attention-based architectures.
  • To identify the most effective model for accurately predicting LH sequence variations.

Main Methods:

  • Protein sequences were pre-processed, numerically encoded, and balanced.
Keywords:
Amino acid sequence analysisAttention mechanismBiLSTMCNNLuteinizing hormone

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  • Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks were implemented.
  • A hybrid CNN + Attention + BiLSTM model was developed and evaluated using stratified training, validation, and testing splits.

    • Main Results:

    • The CNN model achieved 86.07% testing accuracy, while BiLSTM improved to 91.47%.
    • The proposed CNN + Attention + BiLSTM model demonstrated superior performance, achieving 99.42% testing accuracy and 99.60% validation accuracy.
    • The hybrid model also achieved near-perfect precision (0.995), recall (0.99), and F1-score (0.995).

    Conclusions:

    • Attention-based hybrid deep learning architectures are effective in predicting LH sequence variations.
    • The developed framework shows promise for identifying novel protein biomarkers in reproductive health.
    • This approach can enhance diagnostic capabilities for reproductive disorders related to hormonal imbalances.