Translation initiation sites (TISs) are important signals in cDNA sequences. In many previous attempts to predict TISs in cDNA sequences, three major factors affect the prediction performance: the nature of the cDNA s...Translation initiation sites (TISs) are important signals in cDNA sequences. In many previous attempts to predict TISs in cDNA sequences, three major factors affect the prediction performance: the nature of the cDNA sequence sets, the rel- evant features selected, and the classification methods used. In this paper, we examine different approaches to select and integrate relevant features for TIS pre- diction. The top selected significant features include the features from the position weight matrix and the propensity matrix, the number of nucleotide C in the se- quence downstream ATG, the number of downstream stop codons, the number of upstream ATGs, and the number of some amino acids, such as amino acids A and D. With the numerical data generated from these features, different classifi- cation methods, including decision tree, naive Bayes, and support vector machine, were applied to three independent sequence sets. The identified significant features were found to be biologically meaningful, while the experiments showed promising results.展开更多
基金This research was supported by Research Grant No.BM/00/007 from the Biomedical Research Council(BMRC)of the Agency for Science,Technology,and Research(A*Star)and the Ministry of Education in Singapore.
文摘Translation initiation sites (TISs) are important signals in cDNA sequences. In many previous attempts to predict TISs in cDNA sequences, three major factors affect the prediction performance: the nature of the cDNA sequence sets, the rel- evant features selected, and the classification methods used. In this paper, we examine different approaches to select and integrate relevant features for TIS pre- diction. The top selected significant features include the features from the position weight matrix and the propensity matrix, the number of nucleotide C in the se- quence downstream ATG, the number of downstream stop codons, the number of upstream ATGs, and the number of some amino acids, such as amino acids A and D. With the numerical data generated from these features, different classifi- cation methods, including decision tree, naive Bayes, and support vector machine, were applied to three independent sequence sets. The identified significant features were found to be biologically meaningful, while the experiments showed promising results.
