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. 2013 May 8:2013:249034.
doi: 10.1155/2013/249034. Print 2013.

Discovering weighted patterns in intron sequences using self-adaptive harmony search and back-propagation algorithms

Yin-Fu Huang  1 , Chia-Ming Wang, Sing-Wu Liou
Affiliations

Discovering weighted patterns in intron sequences using self-adaptive harmony search and back-propagation algorithms

Yin-Fu Huang et al. ScientificWorldJournal. .

Abstract

A hybrid self-adaptive harmony search and back-propagation mining system was proposed to discover weighted patterns in human intron sequences. By testing the weights under a lazy nearest neighbor classifier, the numerical results revealed the significance of these weighted patterns. Comparing these weighted patterns with the popular intron consensus model, it is clear that the discovered weighted patterns make originally the ambiguous 5SS and 3SS header patterns more specific and concrete.

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Figures

Figure 1
Figure 1
An illustration of pre-mRNA to mature mRNA.
Figure 2
Figure 2
Procedure for discovering weighted patterns.
Figure 3
Figure 3
Tandem repeats of condons from the UFPs and MFPs.
Figure 4
Figure 4
Optimization surface.
Figure 5
Figure 5
Two-layered nearest neighbor classifier.
Figure 6
Figure 6
Error of the conventional k-NN classifier and the weighted k-NN classifier on different k.
Figure 7
Figure 7
F-measure of the conventional k-NN classifier and the weighted k-NN classifier on different k.
Figure 8
Figure 8
Precision of the conventional k-NN classifier and the weighted k-NN classifier on different k.
Figure 9
Figure 9
Recall of the conventional k-NN classifier and the weighted k-NN classifier on different k.
See this image and copyright information in PMC

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