Journal of molecular biology, , Pearson Addison Wesley, Boston. For this purpose, the predictive models of PBOND server have been trained using fully balanced datasets, in which all four classes are equally represented. The user may choose to process either a single sequence or upload multiple sequences for batch processing; there is no upper limit to the number of sequences submitted for batch processing. In the latter, each model independently assigns a label cis-Pro, cis-nonPro, trans-Pro, trans-nonPro to every residue in the query sequence and then a linear time majority voting algorithm calculates the consensus of the 10 predictions. However, the biological significance of cis formations outweighs possible overpredictions. Bioinformatics Oxford, England , 19,
Although the reported results are promising, such refined dataset proline bonds diminishes the credibility of the proposed method. The performance achieved using the initial input vector is in general quite poor, even though voting slightly improves the results. Amino acid sequences can be provided in FASTA format either by pasting them in the text box or uploading them within a text file. Bioinformatics Oxford, England , 16, However, the biological significance of cis formations outweighs possible overpredictions. Nature chemical biology, 3, This could be attributed to the refined length of the sliding window, as well as to the small number of employed features. Journal of molecular biology, , Performance measures using the whole feature vector, with and without performing majority voting. Artificial Intelligence, 97, Furthermore, the reliability index associated with every prediction can be used for post processing the prediction results. Nature structural biology, 5, The user may choose to process either a single sequence or upload multiple sequences for batch processing; there is no upper limit to the number of sequences submitted for batch processing. After the sequence or the sequences are uploaded, several features are extracted. In the latter, each model independently assigns a label cis-Pro, cis-nonPro, trans-Pro, trans-nonPro to every residue in the query sequence and then a linear time majority voting algorithm calculates the consensus of the 10 predictions. Table 3 The performance PBOND compares well with previously published studies, albeit validated on different datasets and using different evaluation methods. The output of the PBOND server consists of a compressed file which contains either a single text file, or multiple text files, in case of batch processing. In Table 2 the performance achieved using the optimal reduced set of features is shown, with and without the employment of the majority voting algorithm. The resulting feature vector consists of attributes. Table 2 It is clear that the feature selection improves to a certain extent the classification outcome; a further increment in the results is achieved using the consensus prediction of the 10 models. TPE and CP provided valuable comments and suggestions throughout the study and helped in the manuscript preparation. More specifically, multiple sequence alignment profiles, in the form of position specific scoring matrices PSSMs are obtained after running PSI-BLAST 8 against one of the provided protein databases; the choice of the database highly affects the computational time, whereas, only slight perturbations are expected in terms of performance. Table 1 presents the performance achieved using the initial feature vector, with and without performing majority voting. Pearson Addison Wesley, Boston. These files contain plain text with the predictions for every sequence uploaded, along with a reliability index for every prediction.
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