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J Theor Biol. 2014 Apr 21;347:84-94. doi: 10.1016/j.jtbi.2014.01.003. Epub 2014 Jan 12.

Robust feature generation for protein subchloroplast location prediction with a weighted GO transfer model.

Li X1, Wu X2, Wu G3.

Author information

1
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, PR China. Electronic address: xiaomeifzu@163.com.
2
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, PR China; Department of Computer Science, University of Vermont, Burlington, VT 50405, USA. Electronic address: xwu@cems.uvm.edu.
3
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, PR China. Electronic address: wugongqing@gmail.com.

Abstract

Chloroplasts are crucial organelles of green plants and eukaryotic algae since they conduct photosynthesis. Predicting the subchloroplast location of a protein can provide important insights for understanding its biological functions. The performance of subchloroplast location prediction algorithms often depends on deriving predictive and succinct features from genomic and proteomic data. In this work, a novel weighted Gene Ontology (GO) transfer model is proposed to generate discriminating features from sequence data and GO Categories. This model contains two components. First, we transfer the GO terms of the homologous protein, and then assign the bit-score as weights to GO features. Second, we employ term-selection methods to determine weights for GO terms. This model is capable of improving prediction accuracy due to the tolerance of the noise derived from homolog knowledge transfer. The proposed weighted GO transfer method based on bit-score and a logarithmic transformation of CHI-square (WS-LCHI) performs better than the baseline models, and also outperforms the four off-the-shelf subchloroplast prediction methods.

KEYWORDS:

Bit-score; Feature generation; Gene ontology; Term-selection method

PMID:
24423409
DOI:
10.1016/j.jtbi.2014.01.003
[Indexed for MEDLINE]
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