| Summary: | 碩士 === 國立中山大學 === 資訊工程學系研究所 === 91 === Recently, the prediction of promoters has attracted many
researchers'' attention. Unfortunately, most previous prediction
algorithms did not provide high enough sensitivity and
specificity. The goal of this thesis is to develop an efficient
prediction algorithm that can increase the detection power (power
= 1 - false negative). We do not try to find more distinct
features in promoters one by one, such as transcriptional
elements. Our main idea is to use the computer power to calculate
all possible patterns which are the possible features of
promoters. Accordingly, we shall define some scoring methods for
training a given set of sequences, which involve promoter
sequences and non-promoter sequences. Then, we can obtain a
threshold value for determining whether a testing sequence is a
promoter or not. By the experimental results, our prediction has
higher correct rate than other previous methods.
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