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The MEGA 5 ClustalW multiple alignment algorithm was used to align all 102 sequences [ 36].
The nucmer alignment algorithm was utilized requiring minimum clusters of 100 bp with maximum gap of 500 bp.
In this study, automatic alignment algorithm was developed for wafer dicing.
The Smith-Waterman local alignment algorithm was used to map the left arm and right arm to segments on the synthetic chromosome and establish new junctions.
A time alignment algorithm was used to compensate for differences in utterance lengths and misalignments between phonemes.
In order to evaluate the applicability of our approach, the proposed alignment algorithm was used in the development of a new gene prediction tool called Exon_Finder2.
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Besides this, alignment algorithm is able to align very long reads, but its performance significantly degrades on long reads (whereas it is quite efficient on short reads).
The default parameters for TrakEM2's non-linear elastic alignment algorithm were sufficient to align the images, producing some deformations that are likely to affect our measurements only slightly and similarly across preparations.
In the proposed algorithm, the input of the sequence alignment algorithm is a pair (text line; fixation line) we want to match.
The input of the sequence alignment algorithm is the whole sequence S and the whole sequence T. The algorithm tries then to find the best alignment between S and T by using the similarity measure.
A segmental variant of the dynamic alignment algorithm is used to find in the phonetic output of an ASR transcript the substring of phonemes that best matches the proper name's phonetization.
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CEO of Professional Science Editing for Scientists @ prosciediting.com