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Published on: October 13, 2023
Gene ontology annotation by density and gravitation models
Wen-Juan Hou1, Kevin Hsin-Yih Lin, Hsin-Hsi Chen
1Department of Computer Science and Information Engineering, National Taiwan University, No.1, Sec.4, Roosevelt Road, Taipei, Taiwan 106. wjhou@nlg.csie.ntu.edu.tw
Summary
Automating gene ontology (GO) annotation is crucial. This study introduces a novel approach using biomedical documents to identify relevant papers for curators, improving the efficiency of gene annotation.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- The Gene Ontology (GO) provides standardized vocabularies for gene product annotation across databases.
- Manual GO annotation of genes from lengthy biomedical literature is time-consuming and labor-intensive.
- Efficient and automated methods for GO annotation are essential for biological research.
Purpose of the Study:
- To develop and evaluate a novel approach for GO annotation that utilizes full-text biomedical documents.
- To improve the efficiency of directing relevant papers to human curators for GO annotation.
- To explore computational methods for enhancing the accuracy and speed of gene-to-GO term assignment.
Main Methods:
- A GO annotation system was developed that analyzes full-text biomedical documents.
- The system explores word density and gravitation relationships between genes and GO terms.
- Multiple density and gravitation models were constructed and evaluated.
Main Results:
- The proposed approach effectively directs curators to more relevant papers for GO annotation.
- Analysis of word density and gravitation relationships aids in identifying gene-GO term associations.
- Evaluation using various criteria demonstrates the efficacy of the developed models.
Conclusions:
- The developed GO annotation approach offers a promising method for accelerating the annotation process.
- Utilizing full-text documents and exploring word relationships can significantly aid curators.
- This work contributes to more efficient and potentially automated gene ontology annotation.
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