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DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms.

Maxat Kulmanov1, Robert Hoehndorf1

  • 1Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.

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Summary
This summary is machine-generated.

DeepGOZero, a novel machine learning model, enables accurate protein function prediction even for functions with limited experimental data. It utilizes ontology embeddings and neural networks for zero-shot learning, advancing computational biology.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning

Background:

  • Protein function prediction is crucial in computational biology.
  • Existing machine learning methods struggle with Gene Ontology (GO) classes having few or no annotations.
  • The Gene Ontology comprises over 50,000 classes and formal axioms.

Purpose of the Study:

  • To develop a machine learning model for improved protein function prediction, particularly for under-annotated GO classes.
  • To enable zero-shot predictions for protein functions using formal axioms within the GO.

Main Methods:

  • Developed DeepGOZero, a machine learning model integrating ontology embeddings and neural networks.
  • Employed a model-theoretic approach for learning ontology embeddings.
  • Leveraged formal axioms within the Gene Ontology for prediction.

Main Results:

  • DeepGOZero successfully improves predictions for protein functions with limited or no experimental annotations.
  • The model enables zero-shot predictions, identifying functions not seen during training.
  • The zero-shot prediction methodology is generalizable to other ontology-based prediction tasks.

Conclusions:

  • DeepGOZero offers a powerful solution for predicting protein functions with sparse annotations.
  • The model's ability to perform zero-shot learning advances the field of computational biology.
  • The developed method has broader applicability in predicting associations with ontology classes.