Protein function prediction with gene ontology: from traditional to deep learning models

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Bioinformatics and Genomics

Main article text

 

Introduction

Survey Methodology

Conventional Approach for Predicting Protein GO Terms

Similarity-based methods

Probabilistic methods

Machine learning methods

GO Term Annotation of Proteins Using Deep Learning Approach

Model-based approach

Supervised learning

Unsupervised learning

Data-based approach

Sequence-based models

Integrated data-based/Structure based-models

Comparison

Set up

Data

Methods

Results

Conclusion

Additional Information and Declarations

Competing Interests

The authors declare there are no competing interests.

Author Contributions

Thi Thuy Duong Vu conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft.

Jaehee Jung conceived and designed the experiments, analyzed the data, authored or reviewed drafts of the paper, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

Supplemental documents are available at GitHub: https://github.com/duongvtt96/Comparison-GO-annotation-systems

Dataset used in this comparison (final benchmark CAFA3) is available at Figshare:

Zhou, Naihui (2019): Supplementary_data. figshare. Dataset. https://doi.org/10.6084/m9.figshare.8135393.v3.

Funding

This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2019R1A2C1084308). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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