Dataset Information

Non-Alignment Features Based Enzyme/Non-Enzyme Classification Using an Ensemble Method.

ABSTRACT: As a growing number of protein structures are resolved without known functions, using computational methods to help predict protein functions from the structures becomes more and more important. Some computational methods predict protein functions by aligning to homologous proteins with known functions, but they fail to work if such homology cannot be identified. In this paper we classify enzymes/non-enzymes using non-alignment features. We propose a new ensemble method that includes three support vector machines (SVM) and two k-nearest neighbor algorithms (k-NN) and uses a simple majority voting rule. The test on a data set of 697 enzymes and 480 non-enzymes adapted from Dobson and Doig shows 85.59% accuracy in a 10-fold cross validation and 86.49% accuracy in a leave-one-out validation. The prediction accuracy is much better than other non-alignment features based methods and even slightly better than alignment features based methods. To our knowledge, our method is the first time to use ensemble methods to classify enzymes/non-enzymes and is superior over a single classifier.

SUBMITTER: Davidson NJ

PROVIDER: S-EPMC3091888 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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Publications

Non-Alignment Features Based Enzyme/Non-Enzyme Classification Using an Ensemble Method.

Davidson Nicholas J NJ Wang Xueyi X

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications 20101201

As a growing number of protein structures are resolved without known functions, using computational methods to help predict protein functions from the structures becomes more and more important. Some computational methods predict protein functions by aligning to homologous proteins with known functions, but they fail to work if such homology cannot be identified. In this paper we classify enzymes/non-enzymes using non-alignment features. We propose a new ensemble method that includes three suppo ...[more]

PMID: 21572553

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Project description:Verbal autopsy (VA) deals with post-mortem surveys about deaths, mostly in low and middle income countries, where the majority of deaths occur at home rather than a hospital, for retrospective assignment of causes of death (COD) and subsequently evidence-based health system strengthening. Automated algorithms for VA COD assignment have been developed and their performance has been assessed against physician and clinical diagnoses. Since the performance of automated classification methods remains low, we aimed to enhance the Naïve Bayes Classifier (NBC) algorithm to produce better ranked COD classifications on 26,766 deaths from four globally diverse VA datasets compared to some of the leading VA classification methods, namely Tariff, InterVA-4, InSilicoVA and NBC. We used a different strategy, by training multiple NBC algorithms using the one-against-all approach (OAA-NBC). To compare performance, we computed the cumulative cause-specific mortality fraction (CSMF) accuracies for population-level agreement from rank one to five COD classifications. To assess individual-level COD assignments, cumulative partially-chance corrected concordance (PCCC) and sensitivity was measured for up to five ranked classifications. Overall results show that OAA-NBC consistently assigns CODs that are the most alike physician and clinical COD assignments compared to some of the leading algorithms based on the cumulative CSMF accuracy, PCCC and sensitivity scores. The results demonstrate that our approach improves the performance of classification (sensitivity) by between 6% and 8% compared with other VA algorithms. Population-level agreements for OAA-NBC and NBC were found to be similar or higher than the other algorithms used in the experiments. Although OAA-NBC still requires improvement for individual-level COD assignment, the one-against-all approach improved its ability to assign CODs that more closely resemble physician or clinical COD classifications compared to some of the other leading VA classifiers.

Dataset Information

Non-Alignment Features Based Enzyme/Non-Enzyme Classification Using an Ensemble Method.

Publications

Non-Alignment Features Based Enzyme/Non-Enzyme Classification Using an Ensemble Method.

Similar Datasets

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