Abstract
Basically, one of the most important issues in identifying biological sequences is accuracy; however, since the exponential growth and excessive diversity of biological data, the requirement to compute within a considerably appropriate time span is usually in conflict with accuracy. We propose a novel approach for accurate identification of DNA sequences in shorter time by discovering sequence patterns – signatures, which are sufficiently distinctive information for the identity of a sequence. The approach is to discover the signatures from the best combination of n-gram patterns and statistics-based models, which are regularly used in the research of Information Retrieval, and then use the signatures to create identifiers. We evaluate the performance of all identifiers on three different types of organisms and three different numbers of identification classes. The experimental results showed that the difference of organisms has no effect on the performance of the proposed model; whereas the different numbers of classes slightly affect the performance. The sole use of Information Gain is changed in a small range of n-grams since the use of its pattern absence brings the unbalanced class and pattern score distribution. However, several identifiers provide over 95% and up to 100% of accuracy, when they are constructed by signatures using the appropriate n-grams and statistics-based models. Our proposed model works well in identifying DNA sequences accurately, and it requires less processing time.
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Keinduangjun, J., Piamsa-nga, P., Poovorawan, Y. (2005). DNA Sequence Identification by Statistics-Based Models. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_134
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DOI: https://doi.org/10.1007/11540007_134
Publisher Name: Springer, Berlin, Heidelberg
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