Abstract
This paper presents a web platform for the detection of peptidases and motifs search from Merops database. The methodology for peptidases detection uses text mining techniques combined with Support Vector Machines (SVM). Preliminary results using two types of SVMs, the C-Support Vector Classification (C-SVC) and One-class SVM, show the feasibility of the methodology. Despite of the best results obtained with C-SVC the One-class SVM can be an alternative solution if only positive examples are available for training.
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Acknowledgments
This work was supported by FCT (Foundation for Science and Technology) and FEDER through Program COMPETE (QREN) under the project FCOMP-01-0124-FEDER-010160 (PTDC/EIA/71770/2006), designated BIOINK – Incremental Kernel Learning for Biological Data Analysis
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Correia, D., Pereira, C., VerÃssimo, P., Dourado, A. (2013). A Platform for Peptidase Detection Based on Text Mining Techniques and Support Vector Machines. In: Madureira, A., Reis, C., Marques, V. (eds) Computational Intelligence and Decision Making. Intelligent Systems, Control and Automation: Science and Engineering, vol 61. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4722-7_42
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DOI: https://doi.org/10.1007/978-94-007-4722-7_42
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