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- W2079890963 abstract "Originating from sequences' length difference, both k-word based methods and graphical representation approaches have uncovered biological information in their distinct ways. However, it is less likely that the mechanisms of information storage vary with sequences' length. A similarity distance suitable for sequences with various lengths will be much near to the mechanisms of information storage. In this paper, new sub-sequences of k-word were extracted from biological sequences under a one-to-one mapping. The new sub-sequences were evaluated by a linear regression model. Moreover, a new distance was defined on the invariants from the linear regression model. With comparison to other alignment-free distances, the results of four experiments demonstrated that our similarity distance was more efficient." @default.
- W2079890963 created "2016-06-24" @default.
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- W2079890963 date "2013-11-01" @default.
- W2079890963 modified "2023-10-17" @default.
- W2079890963 title "Linear regression model of short k-word: a similarity distance suitable for biological sequences with various lengths" @default.
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- W2079890963 doi "https://doi.org/10.1016/j.jtbi.2013.07.028" @default.
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