title: Robust feature generation for protein subchloroplast location prediction with a weighted GO transfer model.
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.
authors.affiliation: School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, PR China. Electronic address: xiaomeifzu@163.com.
authors.forename: Xiaomei
authors.initials: X
authors.lastname: Li
authors.affiliation: 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.
authors.forename: Xindong
authors.initials: X
authors.lastname: Wu
authors.affiliation: School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, PR China. Electronic address: wugongqing@gmail.com.
authors.forename: Gongqing
authors.initials: G
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D002736:Chloroplasts / Q000378:metabolism*; D008954:Models, Biological; D060388:Support Vector Machine
keywords: Bit-score; Feature generation; Gene ontology; Term-selection method