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  1. Home
  3. Melo-ed: Learning Locality-sensitive Multi-embeddings For Edit Distance.
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  3. Melo-ed: Learning Locality-sensitive Multi-embeddings For Edit Distance.

Related Experiment Video

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

994

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

Xin Yuan1,2, Ke Chen1, Ajmain Yasar Ahmed1

  • 1Department of Computer Science and Engineering, The Pennsylvania State University, PA 16803, USA.

Biorxiv : the Preprint Server for Biology
|December 8, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

MELO-ED, a new framework, enhances biological sequence similarity searches by approximating edit distance using multi-dimensional embeddings. This method achieves high accuracy and scalability for massive genomic datasets.

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994

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Edit distance is crucial for biological sequence similarity but computationally expensive.
  • Previous methods like learned locality-sensitive bucketing (LSB) had accuracy limitations due to one-dimensional hash values.
  • Scalable and accurate sequence comparison is vital for large-scale genomic analysis.

Purpose of the Study:

  • Introduce MELO-ED, a novel framework to efficiently approximate edit distance for biological sequences.
  • Improve accuracy and scalability of sequence similarity searches beyond existing methods.
  • Enable fast classification of similar and dissimilar sequences in massive datasets.

Main Methods:

  • Developed MELO-ED, a multi-embedding locality-sensitive framework.
  • Employed a Siamese convolutional neural architecture for learning complementary embeddings.
  • Integrated locality-sensitive bucketing with higher-dimensional embeddings.
  • Utilized mature indexing methods in the embedding space for similarity searches.

    • Main Results:

    • MELO-ED achieves near-perfect accuracy in approximating edit distance.
    • The framework demonstrates superior performance and efficiency compared to traditional and machine learning methods.
    • Evaluations on simulated DNA and real barcode data confirm MELO-ED's effectiveness.
    • MELO-ED significantly speeds up edit distance computations for large databases.

    Conclusions:

    • MELO-ED offers a state-of-the-art solution for fast and accurate biological sequence classification.
    • The multi-embedding approach overcomes limitations of previous LSB functions.
    • MELO-ED enables scalable similarity searches in massive genomic databases.