• Corpus ID: 13267858

Pattern Analysis for the Prediction of Eukoryatic Pro-peptide Cleavage Sites ?

@inproceedings{zr2007PatternAF,
  title={Pattern Analysis for the Prediction of Eukoryatic Pro-peptide Cleavage Sites ?},
  author={S. {\"O}z{\"o}ğ{\"u}r and John Shawe-Taylor and Gerhard-Wilhelm Weber and Z{\"u}mr{\"u}t Beg{\"u}m {\"O}gel},
  year={2007}
}

• S. Özöğür, J. Shawe-Taylor, +1 author Z. Ögel
• Published 2007
• Computer Science

Support vector machines have many applications in analyzing biological data from analysing gene expression arrays to understanding EEG signals of sleep stages. We have developed an application that will allow the prediction of the pro-peptide cleavage site of fungal extracellular proteins which display mostly a monobasic or dibasic processing site. So, one of the critical questions to be answered within the scope of this study, reads: What are the determinants of the cleavage site which can be employed to predict the process? A collection of fungal pro-enzyme sequences are used as a training data set. A specifically designed kernel is expressed as an application of the well known Gaussian kernel via feature spaces defined for our problem. Rather than fixing the kernel parameters with cross validation or other methods, we introduce a novel approach that simultaneously performs model selection together with the test of accuracy and testing confidence levels. This leads us to higher accuracy at significantly reduced training times. The results of the server ProP1.0 which predicts pro-peptide cleavage sites are compared with the results of this study. A similar mathematical approach may be adapted to pro-peptide cleavage prediction in other eukoryates.