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Updated: Sep 15, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

GOAnnotator: accurate protein function annotation using automatically retrieved literature.

Huiying Yan¹, Hancheng Liu¹, Shaojun Wang¹

¹Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China.

Bioinformatics (Oxford, England)

|July 15, 2025

Summary

This summary is machine-generated.

GOAnnotator automates protein function annotation by retrieving relevant literature and identifying Gene Ontology (GO) terms. This novel framework enhances protein function prediction without manual curation.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Automated protein function prediction (AFP) is crucial for biological research.
Current methods, like GORetriever, depend on costly manual literature curation.
Existing approaches cover a limited number of proteins.

Purpose of the Study:

To introduce GOAnnotator, a novel framework for automated protein function annotation.
To overcome limitations of existing AFP methods relying on manual curation.
To improve the efficiency and scope of protein function annotation.

Main Methods:

GOAnnotator integrates PubRetriever for literature retrieval and re-ranking.
It utilizes GORetriever+ for identifying Gene Ontology (GO) terms from retrieved texts.
The framework employs a hybrid approach combining literature retrieval and term identification.

Main Results:

GOAnnotator demonstrated high-quality functional annotations in experiments.
It outperformed the existing GORetriever method in realistic scenarios.
The framework successfully uncovered unique literature and predicted additional protein functions.

Conclusions:

GOAnnotator significantly enhances automated protein function annotation.
It reduces reliance on time-consuming manual literature curation.
The framework has the potential to streamline and broaden protein function annotation efforts.