Abstract
CpG islands (CGIs) are clusters of CpG dinucleotides in GC-rich regions and represent an important gene feature of mammalian genomes. Several algorithms have been developed to identify CGIs. Here we applied Support Vector Machine (SVM), a machine learning approach, to classify CGIs that are associated with the promoter regions of genes. We demonstrated that our SVM-based algorithm had much higher sensitivity and specificity in classifying promoter-associated CGIs than other algorithms, and had high reliability. The advantages of SVM in our method and future improvements were discussed.
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© 2008 Springer-Verlag Berlin Heidelberg
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Han, L., Yang, R., Su, B., Zhao, Z. (2008). An SVM-Based Algorithm for Classifying Promoter-Associated CpG Islands in the Human and Mouse Genomes. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_117
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DOI: https://doi.org/10.1007/978-3-540-85984-0_117
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85983-3
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