Subchloroplast Location Prediction via Homolog Knowledge Transfer and Feature Selection
DOI:
https://doi.org/10.1609/aaai.v27i1.8527Keywords:
Subchloroplast location prediction, Bit-score, Term-selection method, Gene OntologyAbstract
The accuracy of subchloroplast location prediction algorithms often depends on predictive and succinct features derived from proteins. Thus, to improve the prediction accuracy, this paper proposes a novel SubChloroplast location prediction method, called SCHOTS, which integrates the HOmolog knowledge Transfer and feature Selection methods. SCHOTS contains two stages. First, discriminating features are generated by WS-LCHI, a Weighted Gene Ontology (GO) transfer model based on bit-Score of proteins and Logarithmic transformation of CHI-square. Second, the more informative GO terms are selected from the features. Extensive studies conducted on three real datasets demonstrate that SCHOTS outperforms three off-the-shelf subchloroplast prediction methods.