Dataset Information

Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context.

ABSTRACT: BACKGROUND: Enzymes are known as the largest class of proteins and their functions are usually annotated by the Enzyme Commission (EC), which uses a hierarchy structure, i.e., four numbers separated by periods, to classify the function of enzymes. Automatically categorizing enzyme into the EC hierarchy is crucial to understand its specific molecular mechanism. RESULTS: In this paper, we introduce two key improvements in predicting enzyme function within the machine learning framework. One is to introduce the efficient sequence encoding methods for representing given proteins. The second one is to develop a structure-based prediction method with low computational complexity. In particular, we propose to use the conjoint triad feature (CTF) to represent the given protein sequences by considering not only the composition of amino acids but also the neighbor relationships in the sequence. Then we develop a support vector machine (SVM)-based method, named as SVMHL (SVM for hierarchy labels), to output enzyme function by fully considering the hierarchical structure of EC. The experimental results show that our SVMHL with the CTF outperforms SVMHL with the amino acid composition (AAC) feature both in predictive accuracy and Matthew's correlation coefficient (MCC). In addition, SVMHL with the CTF obtains the accuracy and MCC ranging from 81% to 98% and 0.82 to 0.98 when predicting the first three EC digits on a low-homologous enzyme dataset. We further demonstrate that our method outperforms the methods which do not take account of hierarchical relationship among enzyme categories and alternative methods which incorporate prior knowledge about inter-class relationships. CONCLUSIONS: Our structure-based prediction model, SVMHL with the CTF, reduces the computational complexity and outperforms the alternative approaches in enzyme function prediction. Therefore our new method will be a useful tool for enzyme function prediction community.

SUBMITTER: Wang YC

PROVIDER: S-EPMC3121122 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context.

Wang Yong-Cui YC Wang Yong Y Yang Zhi-Xia ZX Deng Nai-Yang NY

BMC systems biology 20110620

<h4>Background</h4>Enzymes are known as the largest class of proteins and their functions are usually annotated by the Enzyme Commission (EC), which uses a hierarchy structure, i.e., four numbers separated by periods, to classify the function of enzymes. Automatically categorizing enzyme into the EC hierarchy is crucial to understand its specific molecular mechanism.<h4>Results</h4>In this paper, we introduce two key improvements in predicting enzyme function within the machine learning framewor ...[more]

PMID: 21689481

Similar Datasets

Project description:BackgroundBreast cancer belongs to the most frequent and severe cancer types in human. Since excretion of modified nucleosides from increased RNA metabolism has been proposed as a potential target in pathogenesis of breast cancer, the aim of the present study was to elucidate the predictability of breast cancer by means of urinary excreted nucleosides.MethodsWe analyzed urine samples from 85 breast cancer women and respective healthy controls to assess the metabolic profiles of nucleosides by a comprehensive bioinformatic approach. All included nucleosides/ribosylated metabolites were isolated by cis-diol specific affinity chromatography and measured with liquid chromatography ion trap mass spectrometry (LC-ITMS). A valid set of urinary metabolites was selected by exclusion of all candidates with poor linearity and/or reproducibility in the analytical setting. The bioinformatic tool of Oscillating Search Algorithm for Feature Selection (OSAF) was applied to iteratively improve features for training of Support Vector Machines (SVM) to better predict breast cancer.ResultsAfter identification of 51 nucleosides/ribosylated metabolites in the urine of breast cancer women and/or controls by LC- ITMS coupling, a valid set of 35 candidates was selected for subsequent computational analyses. OSAF resulted in 44 pairwise ratios of metabolite features by iterative optimization. Based on this approach ultimately estimates for sensitivity and specificity of 83.5% and 90.6% were obtained for best prediction of breast cancer. The classification performance was dominated by metabolite pairs with SAH which highlights its importance for RNA methylation in cancer pathogenesis.ConclusionExtensive RNA-pathway analysis based on mass spectrometric analysis of metabolites and subsequent bioinformatic feature selection allowed for the identification of significant metabolic features related to breast cancer pathogenesis. The combination of mass spectrometric analysis and subsequent SVM-based feature selection represents a promising tool for the development of a non-invasive prediction system.

Dataset Information

Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context.

Publications

Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure