AAAI Publications, Twenty-Seventh AAAI Conference on Artificial Intelligence

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Subchloroplast Location Prediction via Homolog Knowledge Transfer and Feature Selection
Xiaomei Li, Xindong Wu, Gongqing Wu, Xuegang Hu

Last modified: 2013-06-29


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.


Subchloroplast location prediction; Bit-score; Term-selection method; Gene Ontology

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