NDLI: The TF-IDF and Neural Networks Approach for Translation Initiation Site Prediction

Content Provider	IEEE Xplore Digital Library
Author	Kongmanee, T. Vanichayobon, S. Wettayaprasit, W.
Copyright Year	2009
Description	Author affiliation: iSTAR Research Laboratory, Computer Science Department, Prince of Songkla University, Thailand (Vanichayobon, S.) \|\| Artificial Intelligence Research Laboratory, Computer Science Department, Prince of Songkla University, Thailand (Kongmanee, T.; Wettayaprasit, W.)
Abstract	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.
Starting Page	318
Ending Page	322
File Size	107621
Page Count	5
File Format	PDF
ISBN	9781424445196
DOI	10.1109/ICCSIT.2009.5234582
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sequences Laboratories Genomics Artificial neural networks neural networks translation initation sites Computer science Accuracy Neural networks DNA TF-IDF correlation-based feature selection Artificial intelligence Bioinformatics
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Feature Mining and Integration for Improving the Prediction Accuracy of Translation Initiation Sites in Eukaryotic mRNAs

Comparison of feature selection techniques for viral DNA replication origin prediction

High performance of artificial neural network for resolving ambiguous nucleotide problem

192 genome informatics 13: 192–200 (2002) using feature generation and feature selection for accurate prediction of translation initiation sites.

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