ArticlePloS one2014
Gene function prediction based on the Gene Ontology hierarchical structure.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 34 citations in OpenAlex.
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- Downregulation of LRRC19 Is Associated with Poor Prognosis in Colorectal Cancer.Journal of oncology · 2022Article
- Finding Gene Associations by Text Mining and Annotating it with Gene Ontology.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Gene function finding through cross-organism ensemble learning.BioData mining · 2021Article
- Use of Network Pharmacology to Investigate the Mechanism by Which Allicin Ameliorates Lipid Metabolism Disorder in HepG2 Cells.Evidence-based complementary and alternative medicine : eCAM · 2021Article
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- Exploring the Molecular Mechanism of the Drug-Treated Breast Cancer Based on Gene Expression Microarray.Biomolecules · 2019Article
- Searching ECOD for Homologous Domains by Sequence and Structure.Current protocols in bioinformatics · 2018Article
- A novel correlation between ATP5A1 gene expression and progression of human clear cell renal cell carcinoma identified by co‑expression analysis.Oncology reports · 2018Article
- Investigating the microRNA-mRNA regulatory network in acute myeloid leukemia.Oncology letters · 2017Article
- SilencingOncology letters · 2017Article
- Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types.Bioinformatics (Oxford, England) · 2016Article
- Integrating Information in Biological Ontologies and Molecular Networks to Infer Novel Terms.Scientific reports · 2016Article
- A Factor Graph Approach to Automated GO Annotation.PloS one · 2016Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The information of the Gene Ontology annotation is helpful in the explanation of life science phenomena, and can provide great support for the research of the biomedical field. The use of the Gene Ontology is gradually affecting the way people store and understand bioinformatic data. To facilitate the prediction of gene functions with the aid of text mining methods and existing resources, we transform it into a multi-label top-down classification problem and develop a method that uses the hierarchical relationships in the Gene Ontology structure to relieve the quantitative imbalance of positive and negative training samples. Meanwhile the method enhances the discriminating ability of classifiers by retaining and highlighting the key training samples. Additionally, the top-down classifier based on a tree structure takes the relationship of target classes into consideration and thus solves the incompatibility between the classification results and the Gene Ontology structure. Our experiment on the Gene Ontology annotation corpus achieves an F-value performance of 50.7% (precision: 52.7% recall: 48.9%). The experimental results demonstrate that when the size of training set is small, it can be expanded via topological propagation of associated documents between the parent and child nodes in the tree structure. The top-down classification model applies to the set of texts in an ontology structure or with a hierarchical relationship.
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