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- W1799059170 abstract "The intelligent data acquisition in biological sequences is a hard and challenge problem since most biological sequences contain unknowledgeable, diverse and huge data. However, the intelligent data acquisition reduces a demand on the use of high computation methods because the data are more compact and more precise. We propose a novel approach for discovering sequence signatures, which are sufficiently distinctive information in identifying the sequences. The signatures are derived from the best combination of the n-grams and the statistical scoring models. From our experiments in applying them to identify the Influenza virus, we found that the identifiers constructed by too short n-gram signatures and inappropriate scoring models get low efficiency since the inappropriate combinations of n-gram signatures and scoring models bring about unbalanced class and pattern score distribution. However, the other identifiers provide accuracy over 80% and up to 100%, when they apply an appropriate combination. In addition to accomplishing in the signature recognition, our proposed approach also requires low computation time for the biological sequence identification." @default.
- W1799059170 created "2016-06-24" @default.
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- W1799059170 date "2005-01-01" @default.
- W1799059170 modified "2023-09-25" @default.
- W1799059170 title "Intelligent Data Recognition of DNA Sequences Using Statistical Models" @default.
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- W1799059170 doi "https://doi.org/10.1007/11590316_100" @default.
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