Introduction
Introduction
The prediction of the Translation Initiation Site (TIS) in a genomic sequence is an important issue in biological research. Although several methods have been proposed to deal with this problem, there is a great potential for the improvement of the accuracy of these methods.
Due to various reasons, including noise in the data as well as biological reasons, TIS prediction is still an open problem and definitely not a trivial task.
In our research group we have been working on this issue for quite a while and have been experimenting on real world DNA sequences that are publicly available by Kent Ridge Biomedical Data Repository.
Research Results (2005)
We follow a typical
three-step approach in order to increase TIS prediction accuracy. In the
first step, we use a feature generation algorithm we developed. In the
second step, all the candidate features, including some new ones generated
by our algorithm, are ranked according to their impact to the accuracy of
the prediction. Finally, in the third step, a classification model is built
using a number of the top ranked features.
We experiment with various feature sets, feature selection methods and classification algorithms, compare with alternative methods, draw important conclusions and propose improved models with respect to prediction accuracy.
You can find detailed results of our experiments here.