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Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window

Research output: Chapter in Book/Report/Conference proceedingConference paper

Original languageEnglish
Title of host publicationArtificial Intelligence Applications and Innovations, AIAI 2020 IFIP WG 12.5 International Workshops - MHDW 2020 and 5G-PINE 2020, Proceedings
EditorsIlias Maglogiannis, Lazaros Iliadis, Elias Pimenidis
PublisherSPRINGER
Pages213-224
Number of pages12
ISBN (Print)9783030491895
DOIs
Published1 Jan 2020
Event9th Mining Humanistic Data Workshop, MHDW 2020, and the 5th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2020, held as parallel events of the 16th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2020 - Neos Marmaras, Greece
Duration: 5 Jun 20207 Jun 2020

Publication series

NameIFIP Advances in Information and Communication Technology
Volume585 IFIP
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X

Conference

Conference9th Mining Humanistic Data Workshop, MHDW 2020, and the 5th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2020, held as parallel events of the 16th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2020
CountryGreece
CityNeos Marmaras
Period5/06/20207/06/2020

King's Authors

Abstract

Since the discovery of motifs in molecular sequences for real genomic data, research into this phenomenon has attracted increased attention. Motifs are relatively short sequences that are biologically significant. This paper utilises the bioinformatics application of the algorithm outlined in [5], testing it using real genomic data from large sequences. It intends to implement bioinformatics application for real genomic data, in order to discover interesting regions for all maximal motifs, in a sliding window of length ℓ, on a sequence x of length n.

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