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When an object's velocity changes over time, the total distance traveled can be determined by summing small displacement intervals over short increments. This approach approximates the true distance through numerical summation and the use of integral calculus. An estimate of the total displacement can be obtained by measuring velocity at regular intervals and multiplying each value by the corresponding time step.If a runner accelerates over the first three seconds of a race, speed measurements...
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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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Related Experiment Video

Updated: Jan 19, 2026

Assessing Changes in Volatile General Anesthetic Sensitivity of Mice after Local or Systemic Pharmacological Intervention
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Locality-sensitive hashing for the edit distance.

Guillaume Marçais1, Dan DeBlasio1, Prashant Pandey1

  • 1Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.

Bioinformatics (Oxford, England)
|September 13, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces Order Min Hash (OMH), a novel locality-sensitive hashing (LSH) method for efficiently estimating edit distance in bioinformatics. OMH considers k-mer order, improving upon existing methods for sequence alignment and reducing computational load.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Sequence Analysis

Background:

  • Sequence alignment is computationally intensive, necessitating efficient methods for large-scale analysis.
  • Current locality-sensitive hashing (LSH) methods for sequence alignment often ignore k-mer order and use proxies for edit distance.
  • Existing LSH techniques may not accurately capture sequence relationships relevant to edit distance.

Purpose of the Study:

  • To develop a novel LSH method that incorporates the relative ordering of k-mers for more accurate sequence comparison.
  • To address the limitations of current LSH methods in handling edit distance.
  • To improve the efficiency and accuracy of sequence alignment in bioinformatics pipelines.

Main Methods:

  • Introduced Order Min Hash (OMH), a refinement of minHash LSH.
  • OMH accounts for both k-mer content and their relative order within sequences.
  • The method is theoretically guaranteed as a gapped LSH for edit distance.

Main Results:

  • OMH provides a practical LSH method sensitive to edit distance by considering k-mer order.
  • Demonstrated the effectiveness of OMH in estimating the likelihood of sequence alignment.
  • The developed method reduces computational requirements without significant introduction of false negatives.

Conclusions:

  • OMH offers a significant advancement in LSH for sequence alignment by incorporating k-mer order.
  • This method provides a more accurate and computationally efficient approach to estimating edit distance.
  • The availability of the code facilitates further research and application in bioinformatics.