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In this paper, AdaBoost algorithm, a popular and effective prediction method, is applied to predict the subcellular locations of Prokaryotic and Eukaryotic Proteins-a dataset derived from SWISSPROT 33.0. Its prediction ability was evaluated by re-substitution test, Leave-One-Out Cross validation (LOOCV) and jackknife test. By comparing its results with some most popular predictors such as Discriminant Function, neural networks, and SVM, we demonstrated that the AdaBoost predictor outperformed these predictors. As a result, we arrive at the conclusion that AdaBoost algorithm could be employed as a robust method to predict subcellular location. An online web server for predicting subcellular location of prokaryotic and eukaryotic proteins is available at http://chemdata.shu.edu.cn/subcell/ .  相似文献
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采用密度泛函方法研究了从细梗胡枝子中提取的三种新型抗氧化剂的分子结构和活性氧清除机理.研究发现优化后的最优分子构型中都含有分子内氢键,与用二级微扰方法得到的结果一致.根据预测的气相和水相抗氧化剂的键解离能、电离势,以及三种重要活性氧的电子亲和势和氢原子亲和势,详细讨论了三种抗氧化剂的活性氧清除机理并发现其抗氧化活性和实验结果一致.  相似文献
3.
Protein sumoylation is one of the most important post-translational modifications. Accurate prediction of sumoylation sites is very useful for the analysis of proteome. Though the putative motif ΨK XE can be used, optimization of prediction models still remains a challenge. In this study, we developed a prediction system based on feature selection strategy. A total of 1,272 peptides with 14 residues from SUMOsp (Xue et al. [8] Nucleic Acids Res 34:W254–W257, 2006) were investigated in this study, including 212 substrates and 1,060 non-substrates. Among the substrates, only 162 substrates comply to the motif ΨK XE. First, 1,272 substrates were divided into training set and test set. All the substrates were encoded into feature vectors by hundreds of amino acid properties collected by Amino Acid Index Database (AAIndex, http://www.genome.jp/aaindex). Then, mRMR (minimum redundancy–maximum relevance) method was applied to extract the most informative features. Finally, Nearest Neighbor Algorithm (NNA) was used to produce the prediction models. Tested by Leave-one-out (LOO) cross-validation, the optimal prediction model reaches the accuracy of 84.4% for the training set and 76.4% for the test set. Especially, 180 substrates were correctly predicted, which was 18 more than using the motif ΨK XE. The final selected features indicate that amino acid residues with two-residue downstream and one-residue upstream of the sumoylation sites play the most important role in determining the occurrence of sumoylation. Based on the feature selection strategy, our prediction system can not only be used for high throughput prediction of sumoylation sites but also as a tool to investigate the mechanism of sumoylation.  相似文献
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Cai YD  Qian Z  Lu L  Feng KY  Meng X  Niu B  Zhao GD  Lu WC 《Molecular diversity》2008,12(2):131-137
Efficient in silico screening approaches may provide valuable hints on biological functions of the compound-candidates, which could help to screen functional compounds either in basic researches on metabolic pathways or drug discovery. Here, we introduce a machine learning method (Nearest Neighbor Algorithm) based on functional group composition of compounds to the analysis of metabolic pathways. This method can quickly map small chemical molecules to the metabolic pathway that they likely belong to. A set of 2,764 compounds from 11 major classes of metabolic pathways were selected for study. The overall prediction rate reached 73.3%, indicating that functional group composition of compounds was really related to their biological metabolic functions.  相似文献
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