Subchloroplast Location Prediction via Homolog Knowledge Transfer and Feature Selection

Authors

  • Xiaomei Li Hefei University of Technology
  • Xindong Wu Hefei University of Technology
  • Gongqing Wu Hefei University of Technology
  • Xuegang Hu Hefei University of Technology

DOI:

https://doi.org/10.1609/aaai.v27i1.8527

Keywords:

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

Abstract

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.

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Published

2013-06-29

How to Cite

Li, X., Wu, X., Wu, G., & Hu, X. (2013). Subchloroplast Location Prediction via Homolog Knowledge Transfer and Feature Selection. Proceedings of the AAAI Conference on Artificial Intelligence, 27(1), 1631-1632. https://doi.org/10.1609/aaai.v27i1.8527