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Related Experiment Video

Updated: Jun 25, 2026

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Improved algorithms for approximate string matching (extended abstract).

Dimitris Papamichail¹, Georgios Papamichail

¹Department of Computer Science, University of Miami, Coral Gables, Miami, USA. dimitris@cs.miami.edu

BMC Bioinformatics

|February 12, 2009

Summary

This summary is machine-generated.

We developed a novel algorithm for calculating edit distance between two strings. This output-sensitive approach offers improved theoretical time bounds and practical efficiency, particularly for strings of differing lengths.

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Area of Science:

Computational biology
Text processing
Pattern recognition

Background:

Approximate string matching is a fundamental problem with broad applications.
Existing algorithms address variants like sequence comparison and longest common subsequence calculation.

Purpose of the Study:

To design and analyze a new algorithm for solving the edit distance problem.
To improve upon existing theoretical time bounds for edit distance calculation.

Main Methods:

Developed an output-sensitive algorithm for edit distance computation.
Analyzed the algorithm's time complexity and space requirements.

Main Results:

The algorithm solves the edit distance problem in O((s - |n - m|).min(m, n, s) + m + n) time and linear space, where s is the edit distance.
Achieved a worst-case time bound independent of the longest string length, improving theoretical limits.
Demonstrated practical efficiency, especially when comparing strings of significantly different lengths.

Conclusions:

A novel algorithm for edit distance calculation has been designed, analyzed, and implemented.
The algorithm presents both theoretical advancements and practical utility.
Source code is publicly available online.