PUBLICATION
Electronic Gene Ontology annotations created by ARBA machine learning models
- Authors
- ZFIN Staff
- ID
- ZDB-PUB-221108-21
- Date
- 2022
- Source
- Automated Data Submission : (Curation)
- Registered Authors
- Keywords
- none
- MeSH Terms
- none
- PubMed
- none
Citation
ZFIN Staff (2022) Electronic Gene Ontology annotations created by ARBA machine learning models. Automated Data Submission. .
Abstract
Association-Rule-Based Annotator (ARBA) predicts Gene Ontology (GO) terms among other types of functional annotation such as Protein Description (DE), Keywords (KW), Enzyme Commission numbers (EC), subcellular LOcation (LO), etc. For all annotation types, reviewed UniProtKB/Swiss-Prot records having manual annotations as reference data are used to perform the machine learning phase and generate prediction models. For GO terms, ARBA has an additional feature to augment reference data using the relations between GO terms in the GO graph. The data augmentation is based on adding more general annotations into records containing manual GO terms, which will result in richer reference data. The predicted GO terms are then propagated to all unreviewed UniProtKB/TrEMBL proteins that meet the conditions of ARBA models. GO annotations using this technique receive the evidence code Inferred from Electronic Annotation (IEA; ECO:0000501). Links: ARBA documentation at UniProt (https://www.uniprot.org/help/arba), Blog on ARBA (http://insideuniprot.blogspot.com/2020/09/association-rule-based-annotator-arba.html).
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping