DNA存储中Levenshtein距离的深度平方欧几里德近似
Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage
摘要：
在DNA分子中存储信息由于其在寿命、高存储密度和低维护成本方面的优势而引起极大的兴趣。DNA存储管道中的一个关键步骤是根据检索到的DNA序列的相似性对其进行有效聚类。Levenshtein距离是衡量两个DNA序列相似性的最合适的度量，但它的计算复杂性较差，与成熟的聚类算法兼容性较差。在这项工作中，我们提出了一种新的深平方欧几里德嵌入的DNA序列使用暹罗神经网络，平方欧几里德嵌入，卡方回归。Levenshtein距离近似为嵌入向量之间的平方欧氏距离，这是快速计算和聚类算法友好。对该方法进行了理论分析和实验验证。实验结果表明，该算法具有较好的鲁棒性和有效性.
英文摘要：
Storing information in DNA molecules is of great interest because of its advantages in longevity, high storage density, and low maintenance cost. A key step in the DNA storage pipeline is to efficiently cluster the retrieved DNA sequences according to their similarities. Levenshtein distance is the most suitable metric on the similarity between two DNA sequences, but it is inferior in terms of computational complexity and less compatible with mature clustering algorithms. In this work, we propose a novel deep squared Euclidean embedding for DNA sequences using Siamese neural network, squared Euclidean embedding, and chi-squared regression. The Levenshtein distance is approximated by the squared Euclidean distance between the embedding vectors, which is fast calculated and clustering algorithm friendly. The proposed approach is analyzed theoretically and experimentally. The results show that the proposed embedding is efficient and robust.
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