Jul 11, 2022 · The Levenshtein distance is approximated by the squared Euclidean distance between the embedding vectors, which is fast calculated and ...
In this paper, we use deep embedding to calculate the Levenshtein distance on a unique real-world dataset formed by the sequences from DNA storage ex- periments ...
The Levenshtein distance between two sequences is the minimum number of insertions, deletions, or substitutions required to modify one string to the other. Page ...
The Levenshtein distance is approximated by the squared Euclidean distance between the embedding vectors, which is fast calculated and clustering algorithm ...
Jul 11, 2022 · In this paper, raised from DNA storage researches, the deep squared Euclidean embedding (DSEE) of DNA sequences was proposed to approximate the ...
Jul 19, 2022 · Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage. Jul 19, 2022. Speakers. AJXG · Alan J. X. Guo. Speaker · 0 ...
Although the squared Euclidean dis- tance is not a true metric, the application of squared Eu- clidean distance not only offers good approximation preci- sion, ...
People also ask
What is the Levenshtein distance in bioinformatics?
What is the maximum distance for Levenshtein distance?
The Levenshtein distance is approximated by the squared Euclidean distance between the embedding vectors, which is fast calculated and clustering algorithm ...
0 About · Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage. 04:56. Deep Squared Euclidean Approximation to the Levenshtein ...
Deep DNA Storage: Scalable and Robust DNA Storage via Coding Theory and Deep ... Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage.