Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/43027
Title: Translation initiation sites prediction with mixture gaussian models
Authors: Li, G. 
Leong, T.-Y. 
Zhang, L. 
Issue Date: 2004
Source: Li, G.,Leong, T.-Y.,Zhang, L. (2004). Translation initiation sites prediction with mixture gaussian models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3240 : 338-349. ScholarBank@NUS Repository.
Abstract: Translation initiation sites (TIS) are important signals in cDNA sequences. Many research efforts have tried to predict TIS in cDNA sequences. In this paper, we propose using mixture Gaussian models to predict TIS in cDNA sequences. Some new global measures are used to generate numerical features from cDNA sequences, such as the length of the open reading frame downstream from ATG, the number of other ATGs upstream and downstream from the current ATGs, etc. With these global features, the proposed method predicts TIS with sensitivity 98% and specificity 92%. The sensitivity is much better than that from other methods. We attribute the improvement in sensitivity to the nature of the global features and the mixture Gaussian models. © Springer-Verlag 2004.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/43027
ISSN: 03029743
Appears in Collections:Staff Publications

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