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Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window
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], t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256372/ http://dx.doi.org/10.1007/978-3-030-49190-1_19 |
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author | Alshammary, Miznah H. Iliopoulos, Costas S. Mohamed, Manal Vayani, Fatima |
author_facet | Alshammary, Miznah H. Iliopoulos, Costas S. Mohamed, Manal Vayani, Fatima |
author_sort | Alshammary, Miznah H. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7256372 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72563722020-05-29 Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window Alshammary, Miznah H. Iliopoulos, Costas S. Mohamed, Manal Vayani, Fatima Artificial Intelligence Applications and Innovations. AIAI 2020 IFIP WG 12.5 International Workshops Article 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. 2020-05-04 /pmc/articles/PMC7256372/ http://dx.doi.org/10.1007/978-3-030-49190-1_19 Text en © IFIP International Federation for Information Processing 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Alshammary, Miznah H. Iliopoulos, Costas S. Mohamed, Manal Vayani, Fatima Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window |
title | Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window |
title_full | Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window |
title_fullStr | Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window |
title_full_unstemmed | Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window |
title_short | Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window |
title_sort | application and algorithm: maximal motif discovery for biological data in a sliding window |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256372/ http://dx.doi.org/10.1007/978-3-030-49190-1_19 |
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