Dataset Information

Using feature optimization-based support vector machine method to recognize the ?-hairpin motifs in enzymes.

ABSTRACT: ?-Hairpins in enzyme, a kind of special protein with catalytic functions, contain many binding sites which are essential for the functions of enzyme. With the increasing number of observed enzyme protein sequences, it is of especial importance to use bioinformatics techniques to quickly and accurately identify the ?-hairpin in enzyme protein for further advanced annotation of structure and function of enzyme. In this work, the proposed method was trained and tested on a non-redundant enzyme ?-hairpin database containing 2818 ?-hairpins and 1098 non-?-hairpins. With 5-fold cross-validation on the training dataset, the overall accuracy of 90.08% and Matthew's correlation coefficient (Mcc) of 0.74 were obtained, while on the independent test dataset, the overall accuracy of 88.93% and Mcc of 0.76 were achieved. Furthermore, the method was validated on 845 ?-hairpins with ligand binding sites. With 5-fold cross-validation on the training dataset and independent test on the test dataset, the overall accuracies were 85.82% (Mcc of 0.71) and 84.78% (Mcc of 0.70), respectively. With an integration of mRMR feature selection and SVM algorithm, a reasonable high accuracy was achieved, indicating the method to be an effective tool for the further studies of ?-hairpins in enzymes structure. Additionally, as a novelty for function prediction of enzymes, ?-hairpins with ligand binding sites were predicted. Based on this work, a web server was constructed to predict ?-hairpin motifs in enzymes (http://202.207.29.251:8080/).

SUBMITTER: Li D

PROVIDER: S-EPMC5562482 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Publications

Using feature optimization-based support vector machine method to recognize the β-hairpin motifs in enzymes.

Li Dongmei D Hu Xiuzhen X Liu Xingxing X Feng Zhenxing Z Ding Changjiang C

Saudi journal of biological sciences 20161128 6

β-Hairpins in enzyme, a kind of special protein with catalytic functions, contain many binding sites which are essential for the functions of enzyme. With the increasing number of observed enzyme protein sequences, it is of especial importance to use bioinformatics techniques to quickly and accurately identify the β-hairpin in enzyme protein for further advanced annotation of structure and function of enzyme. In this work, the proposed method was trained and tested on a non-redundant enzyme β-ha ...[more]

PMID: 28855832

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Project description:Biological data are accumulating at a faster rate, but interpreting them still remains a problem. Classifying biological data into distinct groups is the first step in understanding them. Data classification in response to a certain treatment is an extremely important aspect for differentially expressed genes in making present/absent calls. Many feature selection algorithms have been developed including the support vector machine recursive feature elimination procedure (SVM-RFE) and its variants. Support vector machine RFEs are greedy methods that attempt to find superlative possible combinations leading to binary classification, which may not be biologically significant. To overcome this limitation of SVM-RFE, we propose a novel feature selection algorithm, termed as "sigFeature" (https://bioconductor.org/packages/sigFeature/), based on SVM and t statistic to discover the differentially significant features along with good performance in classification. The "sigFeature" R package is centered around a function called "sigFeature," which provides automatic selection of features for the binary classification. Using six publicly available microarray data sets (downloaded from Gene Expression Omnibus) with different biological attributes, we further compared the performance of "sigFeature" to three other feature selection algorithms. A small number of selected features (by "sigFeature") also show higher classification accuracy. For further downstream evaluation of its biological signature, we conducted gene set enrichment analysis with the selected features (genes) from "sigFeature" and compared it with the outputs of other algorithms. We observed that "sigFeature" is able to predict the signature of four out of six microarray data sets accurately, whereas the other algorithms predict less data set signatures. Thus, "sigFeature" is considerably better than related algorithms in discovering differentially significant features from microarray data sets.

Dataset Information

Using feature optimization-based support vector machine method to recognize the ?-hairpin motifs in enzymes.

Publications

Using feature optimization-based support vector machine method to recognize the β-hairpin motifs in enzymes.

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