Reversal Distances for Strings with Few Blocks or Small Alphabets

Bulteau, Laurent; Fertin, Guillaume; Komusiewicz, Christian

doi:10.1007/978-3-319-07566-2_6

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…It can be applied, into a certain extent, also to applications like bioinformatics. However, we have excluded measures utilizing application-specific features like reverse distance (Bulteau et al, 2014) and string kernels (Leslie et al, 2002) used in machine learning applications. Nevertheless, the results can still be highly relevant in bioinformatics.…”

Section: Discussionmentioning

confidence: 99%

Framework for syntactic string similarity measures

Gali¹,

Mariescu-Istodor²,

Hostettler³

et al. 2019

Expert Systems with Applications

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights Token-level measures outperform character-level measures when the order of the words varies  Q-grams provide a good compromise between token-and character-level measures  Token-level measures are significantly outperformed by their soft variants  Soft measures based on set-matching methods perform best when using q-gram at the character level  The performance of similarity measures varies depending on the type of the datasets

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Section: Discussionmentioning

confidence: 99%

Framework for syntactic string similarity measures

Gali¹,

Mariescu-Istodor²,

Hostettler³

et al. 2019

Expert Systems with Applications

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show abstract

“…time in both the signed and unsigned variants. [21]. As for DCJ, the variant restricting scenarios to reversals preserving common intervals is also NP-hard, and FPT for a parameter given by the common interval structure [22].…”

Section: Sorting By Reversals (Sbr)mentioning

confidence: 99%

“…No polynomial-time algorithm is known for computing these rearrangement distances, although NP-completeness is still open, notably on permutations for signed prefix reversals and prefix transpositions. Fixed-parameter tractability results may be achieved in the permutation model using the relationship between these rearrangements and breakpoints [24], and in the string model using the number of blocks b max as a parameter [21,25].…”

Section: Sorting By Reversals (Sbr)mentioning

confidence: 99%

Parameterized Algorithms in Bioinformatics: An Overview

Bulteau¹,

Weller²

2019

Algorithms

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Bioinformatics regularly poses new challenges to algorithm engineers and theoretical computer scientists. This work surveys recent developments of parameterized algorithms and complexity for important NP-hard problems in bioinformatics. We cover sequence assembly and analysis, genome comparison and completion, and haplotyping and phylogenetics. Aside from reporting the state of the art, we give challenges and open problems for each topic.

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Prefix and Suffix Reversals on Strings

Fertin

Jankowiak

Jean

2015

String Processing and Information Retrieval

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International audienceThe Sorting by Prefix Reversals problem consists insorting the elements of a given permutation π with a minimum numberof prefix reversals, i.e. reversals that always imply the leftmost elementof π. A natural extension of this problem is to consider strings (inwhich any letter may appear several times) rather than permutations. Instrings, three different types of problems arise: grouping (starting from astring S, transform it so that all identical letters are consecutive), sorting(a constrained version of grouping, in which the target string must belexicographically ordered) and rearranging (given two strings S and T,transform S into T). In this paper, we study these three problems, underan algorithmic viewpoint, in the setting where two operations (ratherthan one) are allowed: namely, prefix and suffix reversals - where a suffixreversal must always imply the rightmost element of the string. Wefirst give elements of comparison between the “prefix reversals only” caseand our case. The algorithmic results we obtain on these three problemsdepend on the size k of the alphabet on which the strings are built. Inparticular, we show that the grouping problem is in P for k ∈ [2; 4] andwhen n − k = O(1), where n is the length of the string. We also showthat the grouping problem admits a PTAS for any constant k, and is2-approximable for any k. Concerning sorting, it is in P for k ∈ [2; 3],admits a PTAS for constant k, and is NP-hard for k = n. Finally, concerningthe rearranging problem, we show that it is NP-hard, both fork = O(1) and k = n. We also show that the three problems are FPTwhen the parameter is the maximum number of blocks over the sourceand target strings

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Reversal Distances for Strings with Few Blocks or Small Alphabets

Cited by 3 publications

References 14 publications

Framework for syntactic string similarity measures

Framework for syntactic string similarity measures

Parameterized Algorithms in Bioinformatics: An Overview

Prefix and Suffix Reversals on Strings

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