Estimating similarity and distance using FracMinHash

We also present frac-kmc, a fast and efficient FracMinHash sketch generator program. frac-kmc is the fastest known FracMinHash sketch generator, delivering accurate and precise results for cosine similarity estimation on real data. frac-kmc is also the first parallel tool for this task, allowing for speeding up sketch generation using multiple CPU cores - an option lacking in existing serialized tools. We show that by computing FracMinHash sketches using frac-kmc, we can estimate pairwise similarity speedily and accurately on real data. frac-kmc is freely available here: https://github.com/KoslickiLab/frac-kmc/.

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Publication Details

Journal
Algorithms for Molecular Biology
Published
2025-05-15
DOI
https://doi.org/10.1186/s13015-025-00276-8
Citations
1
Primary Topic
Gene expression and cancer classification
Type
article
Field-Weighted Citation Impact
0.81

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article

Estimating similarity and distance using FracMinHash

David Koslicki, Mahmudur Rahman Hera
1 citations
Algorithms for Molecular Biology
Gene expression and cancer classification
0.81
article

Estimating similarity and distance using FracMinHash

David Koslicki, Mahmudur Rahman Hera
article en
1 citations

Abstract

We also present frac-kmc, a fast and efficient FracMinHash sketch generator program. frac-kmc is the fastest known FracMinHash sketch generator, delivering accurate and precise results for cosine similarity estimation on real data. frac-kmc is also the first parallel tool for this task, allowing for speeding up sketch generation using multiple CPU cores - an option lacking in existing serialized tools. We show that by computing FracMinHash sketches using frac-kmc, we can estimate pairwise similarity speedily and accurately on real data. frac-kmc is freely available here: https://github.com/KoslickiLab/frac-kmc/.

Algorithms for Molecular BiologyVol. 20(1)
Pennsylvania State University (US)
National Institutes of Health
Openalex Percentile: Top 28%
Gene expression and cancer classification
0.81
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