Abstract
Cytokines are crucial intercellular regulators that have important physiological roles in a wide range of disease processes. The identification of new cytokines by computational methods can provide valuable clues in functional studies of uncharacterized proteins without performing extensive experiments. In this study, we developed a new prediction method for the cytokine family based on dipeptide composition and length distribution by using support vector machine (SVM). The cross-validation results demonstrated that cytokines could be correctly identified with an accuracy of 97% at family classification and 90% at subfamily recognition correctly, respectively. In comparison with existing methods in the literature, the present method displayed great competitiveness on identifying cytokines correctly.
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He, W., Jiang, Z., Li, Z. (2008). Predicting Cytokines Based on Dipeptide and Length Feature. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_12
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DOI: https://doi.org/10.1007/978-3-540-87442-3_12
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