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Protein-protein interaction detection using deep learning: A survey, comparative analysis, and experimental

Kamal Taha1

  • 1Department of Computer Science, Khalifa University, Abu Dhabi, United Arab Emirates.

Computers in Biology and Medicine
|December 7, 2024
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Summary
This summary is machine-generated.

This survey analyzes Deep Learning (DL) for protein-protein interaction (PPI) detection. Deep Neural Networks (DNNs) show high accuracy but lack interpretability, while other DL models offer specific advantages for PPI identification.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Artificial Intelligence

Background:

  • Protein-protein interactions (PPIs) are crucial for cellular functions.
  • Accurate identification of PPIs is essential for understanding biological processes.
  • Traditional methods for PPI detection have limitations in scalability and accuracy.

Purpose of the Study:

  • To comprehensively analyze Deep Learning (DL) techniques for protein-protein interaction (PPI) detection.
  • To evaluate the scalability, interpretability, accuracy, and efficiency of various DL algorithms.
  • To provide empirical and experimental assessments of DL methods in PPI identification.

Main Methods:

  • Systematic review of Deep Learning (DL) algorithms applied to protein-protein interaction (PPI) detection.
  • Empirical assessment based on four key criteria: scalability, interpretability, accuracy, and efficiency.
  • Experimental evaluation and ranking of specific algorithms and broader methodological categories.

Main Results:

  • Deep Neural Networks (DNNs) achieved high accuracy but suffered from overfitting and low interpretability.
  • Convolutional Neural Networks (CNNs) excelled in extracting hierarchical features from biological sequences.
  • Generative Stochastic Networks (GSNs) demonstrated strength in handling uncertainty, while Long Short-Term Memory (LSTM) networks captured temporal dependencies but faced scalability issues.

Conclusions:

  • Deep Learning (DL) offers powerful tools for advancing protein-protein interaction (PPI) identification.
  • Different DL architectures present unique strengths and weaknesses for PPI detection.
  • Future research should focus on optimizing DL techniques for enhanced performance, interpretability, and scalability in PPI analysis.