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Automatic Gene Function Prediction in the 2020's.

Stavros Makrodimitris1,2, Roeland C H J van Ham1,2, Marcel J T Reinders1,3

  • 1Delft Bioinformatics Lab, Delft University of Technology, 2628XE Delft, The Netherlands.

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|October 30, 2020
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Summary
This summary is machine-generated.

Automatic Function Prediction (AFP) methods struggle to keep pace with new sequence data. Addressing challenges in annotation, data sources, and algorithms can significantly advance computational biology.

Keywords:
Gene Ontologyautomatic function predictionmachine learningprotein representation

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The rapid generation of DNA and protein sequences outpaces experimental functional discovery.
  • Accurate Automatic Function Prediction (AFP) is crucial for understanding biological data.
  • Despite progress, AFP remains an unsolved challenge in bioinformatics.

Purpose of the Study:

  • To identify key challenges hindering the advancement of AFP.
  • To propose strategies for improving AFP applicability and performance.
  • To highlight the potential of machine learning in future AFP development.

Main Methods:

  • Discussing challenges in condition-specific functional annotation.
  • Addressing the prediction of functions for non-model organisms.
  • Incorporating novel data sources and mitigating Gene Ontology (GO) annotation biases.
  • Optimizing the representation of proteins, genes, and functions for prediction algorithms.

Main Results:

  • Identifying five critical challenges in current AFP research.
  • Recommending adaptations in data representation and algorithmic approaches.
  • Demonstrating the need for continued machine learning integration in AFP.

Conclusions:

  • AFP is a dynamic research area with significant potential for advancement.
  • Overcoming identified challenges will enhance the utility of AFP.
  • Machine learning innovations are key to the next generation of AFP in computational biology.