The TF-IDF and Neural Networks Approach for Translation Initiation Site Prediction
The precise prediction of translation initiation site is an important task for the analysis of genomic sequence. This study aims to increase the accuracy for the prediction of translation initiation site using a TF-IDF-NN-TIS model (TF-IDF and Neural Networks Approach for Translation Initiation Site Prediction). This study creates feature using 1-gram and 2-gram techniques for both upstream and downstream. Determining feature value uses TF-IDF approach and feature selection by correlation-based feature selection method. Evaluation prediction results use 10-fold cross validation. This study performed experiments on three different datasets that are Vertebrate, Arabidopsis thaliana, and TIS+50.The results of the study indicate that the proposed model gives highest accuracy with less processing time.
translation initation sites neural networks correlation-based feature selection TF-IDF
Tarintorn Kongmanee Sirirut Vanichayobon Wiphada Wettayaprasit
Artificial Intelligence Research Laboratory Computer Science Department Prince of Songkla University iSTAR Research Laboratory Computer Science Department Prince of Songkla University, Thailand
国际会议
北京
英文
979-983
2009-08-08（万方平台首次上网日期，不代表论文的发表时间）