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Gene Ontology graph embeddings with Dynamic Thresholding based Deep Neural Networks for Multi-label protein

Harsha Vardhan Chirumamilla1, Tejus Paturu1, Naga Raju Reddy Maruprolu1

  • 1Department of Computer Science and Engineering, National Institute of Technology Puducherry, Karaikal, 609609, Puducherry, India.

Computational Biology and Chemistry
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Summary

This study introduces a novel two-step method for predicting protein subcellular localization using Gene Ontology embeddings and dynamic thresholding Deep Neural Networks. The approach significantly improves accuracy in identifying multiple protein locations, outperforming existing state-of-the-art models.

Keywords:
Deep neural networksGO graphGene ontologyMulti-label classificationNode embeddingsNode2VecProtein subcellular localization

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Protein subcellular localization is crucial for cellular function.
  • Traditional methods for localization are time-consuming.
  • Machine learning and deep learning offer enhanced prediction accuracy.

Purpose of the Study:

  • To develop a more efficient and accurate method for protein subcellular localization prediction.
  • To leverage Gene Ontology (GO) embeddings and deep neural networks for improved predictions.
  • To enable accurate multi-label classification of protein locations.

Main Methods:

  • Feature extraction using node embeddings from Gene Ontology graphs.
  • Capturing hierarchical relationships within GO terms (molecular functions, biological processes, cellular components).
  • Multi-label classification with a dynamic thresholding Deep Neural Network.

Main Results:

  • Achieved 44.69% Overall Actual Accuracy and 91.21% Relaxed Accuracy on the DeepLoc 2.0 dataset.
  • Achieved 82.43% Overall Actual Accuracy and 97.25% Relaxed Accuracy on the Plant-mSubP dataset.
  • Demonstrated significant improvements over state-of-the-art models, with minimum increases of 5.69% (Overall Actual Accuracy) and 12.67% (Relaxed Accuracy).

Conclusions:

  • The proposed two-step approach effectively combines GO embeddings and dynamic thresholding for superior multi-label protein localization.
  • The model significantly enhances prediction accuracy compared to existing methods.
  • This advancement offers a more efficient tool for understanding protein functions within cellular environments.