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

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window
Creator	Alshammary, Miznah Iliopoulos, Costas Mohamed, Manal Vayani, Fatima
Source	PMC
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.
has issue date	2020-05-04(xsd:dateTime)
bibo:doi	10.1007/978-3-030-49190-1_19
has license	no-cc
sha1sum (hex)	b6381faf26d63f0e12d5bae37df81482e9474d0b
schema:url	https://doi.org/10.1007/978-3-030-49190-1_19
resource representing a document's title	Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window
has PubMed Central identifier	PMC7256372
schema:publication	Artificial Intelligence Applications and Innovations
resource representing a document's body	covid:b6381faf26d63f0e12d5bae37df81482e9474d0b#body_text
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