### Abstract

If the genetic maps of two species are modelled as permutations of (homologous) genes, the number of chromosomal rearrangements in the form of deletions, block moves, inversions etc. to transform one such permutation to another can be used as a measure of their evolutionary distance. Motivated by such scenarios, we study problems of computing distances between permutations as well as matching permutations in sequences, and finding most similar permutation from a collection (nearest neighbor). We adopt a general approach: embed permutation distances of relevance into well-known vector spaces in an approximately distance-preserving manner, and solve the resulting problems on the well-known spaces. Our results are as follows: We present the first known approximately distance preserving embeddings of these permutation distances into well-known spaces. Using these embeddings, we obtain several results, including the first known ecient solution for approximately solving nearest neighbor problems with permutations and the first known algorithms for finding permutation distances in the data stream model. We consider a novel class of problems called permutation matching problems which are similar to string matching problems, except that the pattern is a permutation (rather than a string) and present linear or near-linear time algorithms for approximately solving permutation matching problems; in contrast, the corresponding string problems take significantly longer.

Original language | English (US) |
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Title of host publication | Automata, Languages and Programming - 28th International Colloquium, ICALP 2001, Proceedings |

Pages | 481-492 |

Number of pages | 12 |

State | Published - Dec 1 2001 |

Event | 28th International Colloquium on Automata, Languages and Programming, ICALP 2001 - Crete, Greece Duration: Jul 8 2001 → Jul 12 2001 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 2076 LNCS |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Other

Other | 28th International Colloquium on Automata, Languages and Programming, ICALP 2001 |
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Country | Greece |

City | Crete |

Period | 7/8/01 → 7/12/01 |

### Fingerprint

### ASJC Scopus subject areas

- Theoretical Computer Science
- Computer Science(all)

### Cite this

*Automata, Languages and Programming - 28th International Colloquium, ICALP 2001, Proceedings*(pp. 481-492). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2076 LNCS).

**Permutation editing and matching via embeddings.** / Cormode, Graham; Muthukrishnan, Shanmugavelayutham; Sahinalp, Süleyman Cenk.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Automata, Languages and Programming - 28th International Colloquium, ICALP 2001, Proceedings.*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2076 LNCS, pp. 481-492, 28th International Colloquium on Automata, Languages and Programming, ICALP 2001, Crete, Greece, 7/8/01.

}

TY - GEN

T1 - Permutation editing and matching via embeddings

AU - Cormode, Graham

AU - Muthukrishnan, Shanmugavelayutham

AU - Sahinalp, Süleyman Cenk

PY - 2001/12/1

Y1 - 2001/12/1

N2 - If the genetic maps of two species are modelled as permutations of (homologous) genes, the number of chromosomal rearrangements in the form of deletions, block moves, inversions etc. to transform one such permutation to another can be used as a measure of their evolutionary distance. Motivated by such scenarios, we study problems of computing distances between permutations as well as matching permutations in sequences, and finding most similar permutation from a collection (nearest neighbor). We adopt a general approach: embed permutation distances of relevance into well-known vector spaces in an approximately distance-preserving manner, and solve the resulting problems on the well-known spaces. Our results are as follows: We present the first known approximately distance preserving embeddings of these permutation distances into well-known spaces. Using these embeddings, we obtain several results, including the first known ecient solution for approximately solving nearest neighbor problems with permutations and the first known algorithms for finding permutation distances in the data stream model. We consider a novel class of problems called permutation matching problems which are similar to string matching problems, except that the pattern is a permutation (rather than a string) and present linear or near-linear time algorithms for approximately solving permutation matching problems; in contrast, the corresponding string problems take significantly longer.

AB - If the genetic maps of two species are modelled as permutations of (homologous) genes, the number of chromosomal rearrangements in the form of deletions, block moves, inversions etc. to transform one such permutation to another can be used as a measure of their evolutionary distance. Motivated by such scenarios, we study problems of computing distances between permutations as well as matching permutations in sequences, and finding most similar permutation from a collection (nearest neighbor). We adopt a general approach: embed permutation distances of relevance into well-known vector spaces in an approximately distance-preserving manner, and solve the resulting problems on the well-known spaces. Our results are as follows: We present the first known approximately distance preserving embeddings of these permutation distances into well-known spaces. Using these embeddings, we obtain several results, including the first known ecient solution for approximately solving nearest neighbor problems with permutations and the first known algorithms for finding permutation distances in the data stream model. We consider a novel class of problems called permutation matching problems which are similar to string matching problems, except that the pattern is a permutation (rather than a string) and present linear or near-linear time algorithms for approximately solving permutation matching problems; in contrast, the corresponding string problems take significantly longer.

UR - http://www.scopus.com/inward/record.url?scp=84879509047&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84879509047&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84879509047

SN - 3540422870

SN - 9783540422877

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 481

EP - 492

BT - Automata, Languages and Programming - 28th International Colloquium, ICALP 2001, Proceedings

ER -