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Approche basée sur MapReduce pour la plus longue sous-séquence commune dans les séquences biologiques

Approche basée sur MapReduce pour la plus longue sous-séquence commune dans les séquences biologiques

Jnaneshwar Bohara / Shashidhar Ram Joshi

60,13 €
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Editorial:
KS OmniScriptum Publishing
Año de edición:
2025
Materia
Informática: cuestiones generales
ISBN:
9786208459765
60,13 €
IVA incluido
Disponible

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Sobre IBD

L’identification de la plus longue sous-séquence commune (LCS) des séquences biologiques a des applications importantes en bio-informatique. En raison de la croissance émergente des applications bio-informatiques, de nouvelles séquences biologiques plus longues ont été utilisées pour le traitement, ce qui représente un défi de taille pour les algorithmes LCS séquentiels. Quelques algorithmes LCS parallèles ont été proposés, mais leur efficacité et leur efficience ne sont pas satisfaisantes compte tenu de la complexité et de la taille croissantes des données biologiques. Afin de surmonter les limites des algorithmes LCS existants et compte tenu du modèle de programmation MapReduce comme technologie prometteuse pour un calcul parallèle haute performance et rentable, un algorithme parallèle basé sur MapReduce a été développé pour LCS. Cette approche adopte les concepts de tables de successeurs, de paires de caractères identiques, d’arbre de successeurs et de traversée de l’arbre de successeurs pour trouver la plus longue sous-séquence commune. Le cadre Hadoop est utilisé pour la réalisation du modèle MapReduce.

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