Browsing by Author "Ho, Shih-Wen"

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ProLoc-GO: Utilizing informative Gene Ontology terms for sequence-based prediction of protein subcellular localization
(2008-02-01)
Huang, Wen-Lin
;
Tung, Chun-Wei
;
Ho, Shih-Wen
;
Hwang, Shiow-Fen
;
Ho, Shinn-Ying
Background: Gene Ontology (GO) annotation, which describes the function of genes and gene products across species, has recently been used to predict protein subcellular and subnuclear localization. Existing GO-based prediction methods for protein subcellular localization use the known accession numbers of query proteins to obtain their annotated GO terms. An accurate prediction method for predicting subcellular localization of novel proteins without known accession numbers, using only the input sequence, is worth developing. Results: This study proposes an efficient sequence-based method (named ProLoc-GO) by mining informative GO terms for predicting protein subcellular localization. For each protein, BLAST is used to obtain a homology with a known accession number to the protein for retrieving the GO annotation. A large number n of all annotated GO terms that have ever appeared are then obtained from a large set of training proteins. A novel genetic algorithm based method (named GOmining) combined with a classifier of support vector machine (SVM) is proposed to simultaneously identify a small number m out of the n GO terms as input features to SVM, where m << n. The m informative GO terms contain the essential GO terms annotating subcellular compartments such as GO: 0005634 (Nucleus), GO: 0005737 (Cytoplasm) and GO: 0005856 (Cytoskeleton). Two existing data sets SCL12 (human protein with 12 locations) and SCL16 (Eukaryotic proteins with 16 locations) with < 25% sequence identity are used to evaluate ProLoc-GO which has been implemented by using a single SVM classifier with the m = 44 and m = 60 informative GO terms, respectively. ProLoc-GO using input sequences yields test accuracies of 88.1% and 83.3% for SCL12 and SCL16, respectively, which are significantly better than the SVM-based methods, which achieve < 35% test accuracies using amino acid composition (AAC) with acid pairs and AAC with dipedtide composition. For comparison, ProLoc-GO using known accession numbers of query proteins yields test accuracies of 90.6% and 85.7%, which is also better than Hum-PLoc (85.0%) and Euk-OET-PLoc (83.7%) using ensemble classifiers with hybridization of GO terms and amphiphilic pseudo amino acid composition for SCL12 and SCL16, respectively. Conclusion: The growth of Gene Ontology in size and popularity has increased the effectiveness of GO-based features. GOmining can serve as a tool for selecting informative GO terms in solving sequence-based prediction problems. The prediction system using ProLoc-GO with input sequences of query proteins for protein subcellular localization has been implemented (see Availability).
ProLoc-rGO: Using rule-based knowledge with Gene Ontology terms for prediction of protein subnuclear localization
(2008)
Huang, Wen-Lin
;
Tung, Chun-Wei
;
Ho, Shih-Wen
;
Ho, Shinn-Ying
;
生物科技學系
Gene Ontology (GO) annotation is a controlled vocabulary of terms and phrases describing the function of genes and gene products, which has been succeeded in predicting subcellualr and subnuclear localization. Generally, each gene product is annotated by very few GO terms from more than 25,000 annotations available at present. How to represent a protein sequence using GO terms as features plays an important role in designing prediction systems for protein subnuclear localization. Our previous work ProLoc-GO can select a small number m out of a large number n GO terms, where m n. However, its off-line time for training is large up to several days even though running on high speedily PC clusters. Therefore, this study proposes an efficient system (ProLoc-rGO) by using the decision tree method to speedily mine m informative GO terms and acquire interpretable rule-based knowledge for predicting subnuclear localization. The ProLoc-rGO performing on SNL9_80 (714 proteins in nine compartments with <80 identity) can mine m=17 informative GO terms, 17 interpretable rules and yield training and test accuracies of 84.9% and 78.2%. For comparison, an accuracy 82.6% (Matthews correlation coefficient (MCC) = 0.711) for ProLoc-rGO performed on SNL9_80 (714 proteins in nine compartments with <80 identity) is obtained, which is better than 67.4% (MCC = 0.50) for Nuc-PLoc that fuses the pseudo-amino acid composition of a protein and its position-specific scoring matrix.