TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
Increasing Splicing Site Prediction by Training Gene Set Based on Species |
| 영문제목(English Title) |
Increasing Splicing Site Prediction by Training Gene Set Based on Species |
| 저자(Author) |
Beunguk Ahn
Elbashir Abbas
Jin-Ah Park
Ho-Jin Choi
|
| 원문수록처(Citation) |
VOL 06 NO. 11 PP. 2784 ~ 2799 (2012. 11) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Biological data have been increased exponentially in recent years, and analyzing these data using data mining tools has become one of the major issues in the bioinformatics research community. This paper focuses on the protein construction process in higher organisms where the deoxyribonucleic acid, or DNA, sequence is filtered. In the process, “unmeaningful” DNA sub-sequences (called introns) are removed, and their meaningful counterparts (called exons) are retained. Accurate recognition of the boundaries between these two classes of sub-sequences, however, is known to be a difficult problem. Conventional approaches for recognizing these boundaries have sought for solely enhancing machine learning techniques, while inherent nature of the data themselves has been overlooked. In this paper we present an approach which makes use of the data attributes inherent to species in order to increase the accuracy of the boundary recognition. For experimentation, we have taken the data sets for four different species from the University of California Santa Cruz (UCSC) data repository, divided the data sets based on the species types, then trained a preprocessed version of the data sets on neural network(NN)-based and support vector machine(SVM)-based classifiers. As a result, we have observed that each species has its own specific features related to the splice sites, and that it implies there are related distances among species. To conclude, dividing the training data set based on species would increase the accuracy of predicting splicing junction and propose new insight to the biological research. |
| 키워드(Keyword) |
Splice sites prediction
exon
intron
species
machine learning
DNA sequence
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| 파일첨부 |
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