Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3

Publications

3 Sequence-based Prediction of Gamma-turn Types using a Physicochemical Property-based Decision Tree Method

Authors: Chyn Liaw, Chun-Wei Tung, Shinn-Jang Ho, Shinn-Ying Ho

Abstract:

The γ-turns play important roles in protein folding and molecular recognition. The prediction and analysis of γ-turn types are important for both protein structure predictions and better understanding the characteristics of different γ-turn types. This study proposed a physicochemical property-based decision tree (PPDT) method to interpretably predict γ-turn types. In addition to the good prediction performance of PPDT, three simple and human interpretable IF-THEN rules are extracted from the decision tree constructed by PPDT. The identified informative physicochemical properties and concise rules provide a simple way for discriminating and understanding γ-turn types.

Keywords: Physicochemical properties, protein secondary structure, Classification and regression tree (CART), γ-turn

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2 Prediction of Protein Subchloroplast Locations using Random Forests

Authors: Chun-Wei Tung, Chyn Liaw, Shinn-Jang Ho, Shinn-Ying Ho

Abstract:

Protein subchloroplast locations are correlated with its functions. In contrast to the large amount of available protein sequences, the information of their locations and functions is less known. The experiment works for identification of protein locations and functions are costly and time consuming. The accurate prediction of protein subchloroplast locations can accelerate the study of functions of proteins in chloroplast. This study proposes a Random Forest based method, ChloroRF, to predict protein subchloroplast locations using interpretable physicochemical properties. In addition to high prediction accuracy, the ChloroRF is able to select important physicochemical properties. The important physicochemical properties are also analyzed to provide insights into the underlying mechanism.

Keywords: Physicochemical properties, random forests, chloroplast, Proteinlocations

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1 Virulent-GO: Prediction of Virulent Proteins in Bacterial Pathogens Utilizing Gene Ontology Terms

Authors: Chia-Ta Tsai, Wen-Lin Huang, Shinn-Jang Ho, Li-Sun Shu, Shinn-Ying Ho

Abstract:

Prediction of bacterial virulent protein sequences can give assistance to identification and characterization of novel virulence-associated factors and discover drug/vaccine targets against proteins indispensable to pathogenicity. Gene Ontology (GO) annotation which describes functions of genes and gene products as a controlled vocabulary of terms has been shown effectively for a variety of tasks such as gene expression study, GO annotation prediction, protein subcellular localization, etc. In this study, we propose a sequence-based method Virulent-GO by mining informative GO terms as features for predicting bacterial virulent proteins. Each protein in the datasets used by the existing method VirulentPred is annotated by using BLAST to obtain its homologies with known accession numbers for retrieving GO terms. After investigating various popular classifiers using the same five-fold cross-validation scheme, Virulent-GO using the single kind of GO term features with an accuracy of 82.5% is slightly better than VirulentPred with 81.8% using five kinds of sequence-based features. For the evaluation of independent test, Virulent-GO also yields better results (82.0%) than VirulentPred (80.7%). When evaluating single kind of feature with SVM, the GO term feature performs much well, compared with each of the five kinds of features.

Keywords: prediction, Bacterial virulence factors, GO terms, protein sequence

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