MELO-ED: learning locality-sensitive multi-embeddings for edit distance.

ABSTRACT: Edit distance is a fundamental metric for quantifying similarity between biological sequences, but its high computational cost limits large-scale applications. Previously, we proposed learned locality-sensitive bucketing (LSB) functions that achieved superior performance and efficiency compared to classical seeding methods for identifying similar and dissimilar sequences. However, each component of an LSB function is represented as a one-dimensional hash value that can only be compared for identity, which constrains the method's accuracy. Here, we introduce MELO-ED, a multi-embedding locality-sensitive framework that upgrades each hash value to a higher-dimensional embedding capable of efficiently approximating edit distance. MELO-ED employs a Siamese convolutional neural architecture that

SUBMITTER: Yuan X 

PROVIDER: S-EPMC12676541 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature