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In addition, we calculate the variance of N for contiguous k-mers, as well as for spaced words using our single and multiple pattern approaches.
By contrast, a substantial improvement could be achieved by using our multiple-pattern approach.
By contrast, we obtained a significant improvement by using our multiple-pattern approach.
The three competing alignment-free methods were clearly outperformed by our multiple-pattern approach.
Also, the variance is higher for the single-pattern approach than for the multiple-pattern approach.
On these benchmark data, the results of our multiple-pattern approach were best when patterns of varying length were combined.
In addition, we applied the Euclidean distance to the relative-frequency vectors obtained with our multiple-pattern approach.
In our multiple-pattern approach, we need to calculate spaced-word frequencies for a large number of patterns.
Moreover, with our multiple-pattern approach the variance of the normalized number of spaced-word matches is further reduced.
As with the real-world protein sequence sets, the multiple-pattern approach with varying pattern lengths gave the best results.
Moreover, we generated pattern sets with 100 randomly selected patterns of weight k and with varying length ℓ for the multiple-pattern approach.
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