A Semi-supervised SVM Based Incorporation Prior Biological Knowledge for Recognizing Translation Initiation Sites
Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.447
In this study, we propose a Semi-Supervised Support Vector Machine (S3VM) based incorporation prior biological knowledge for recognizing translation initiation sites (TISs). The task of finding TIS can be modeled as a classification problem. S3VM builds a SVM classifier based on small amounts of labeled data and large amounts of unlabeled data, incorporates prior biological knowledge by engineering an appropriate kernel function with a batch-mode incremental training method. The algorithm has been implemented and tested on previously published data. Our experimental results on real nucleotide sequences data show that our methods improve the prediction accuracy greatly and our method performs significantly better than ESTSCAN and SVMs with Salzberg kernel.
S3VM, finding TIS, classification problem, kernel function, batch-mode incremental
J. Huang, Y. Ou, F. Wang and M. Zhou, "A Semi-supervised SVM Based Incorporation Prior Biological Knowledge for Recognizing Translation Initiation Sites," 2009 WRI World Congress on Computer Science and Information Engineering, CSIE(CSIE), Los Angeles, CA, 2009, pp. 544-548.