Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences

Please use this identifier to cite or link to this item: https://doi.org/10.1109/TKDE.2005.133

Title:	Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences
Authors:	Li, G. Leong, T.-Y. Zhang, L.
Keywords:	Bioinformatics Classification Feature extraction Mixture Gaussian model Translation initiation sites
Issue Date:	2005
Citation:	Li, G., Leong, T.-Y., Zhang, L. (2005). Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences. IEEE Transactions on Knowledge and Data Engineering 17 (8) : 1152-1160. ScholarBank@NUS Repository. https://doi.org/10.1109/TKDE.2005.133
Abstract:	Translation initiation sites (TISs) are important signals in cDNA sequences. Many research efforts have tried to predict TISs in cDNA sequences. In this paper, we propose to use mixture Gaussian models for TIS prediction. Using both local features and some features generated from global measures, the proposed method predicts TISs with a sensitivity of 98 percent and a specificity of 93.6 percent. Our method outperforms many other existing methods in sensitivity while keeping specificity high. We attribute the improvement in sensitivity to the nature of the global features and the mixture Gaussian models. © 2005 IEEE.
Source Title:	IEEE Transactions on Knowledge and Data Engineering
URI:	http://scholarbank.nus.edu.sg/handle/10635/43121
ISSN:	10414347
DOI:	10.1109/TKDE.2005.133
Appears in Collections:	Staff Publications

There are no files associated with this item.

Check