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

Miznah H. Alshammary; Costas S. Iliopoulos; Manal Mohamed; Fatima Vayani

doi:10.1007/978-3-030-49190-1_19

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

Miznah H. Alshammary^*, Costas S. Iliopoulos, Manal Mohamed, Fatima Vayani

^*Corresponding author for this work

King's College London

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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.

Original language	English
Title of host publication	Artificial Intelligence Applications and Innovations, AIAI 2020 IFIP WG 12.5 International Workshops - MHDW 2020 and 5G-PINE 2020, Proceedings
Editors	Ilias Maglogiannis, Lazaros Iliadis, Elias Pimenidis
Publisher	SPRINGER
Pages	213-224
Number of pages	12
ISBN (Print)	9783030491895
DOIs	https://doi.org/10.1007/978-3-030-49190-1_19
Publication status	Published - 1 Jan 2020
Event	9th 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 2020 → 7 Jun 2020

Publication series

Name	IFIP Advances in Information and Communication Technology
Volume	585 IFIP
ISSN (Print)	1868-4238
ISSN (Electronic)	1868-422X

Conference

Conference	9th 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
Country/Territory	Greece
City	Neos Marmaras
Period	5/06/2020 → 7/06/2020

Keywords

Bioinformatics application
Motif discovery
Sequence motifs

Access to Document

10.1007/978-3-030-49190-1_19

Cite this

Alshammary, M. H., Iliopoulos, C. S., Mohamed, M., & Vayani, F. (2020). Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window. In I. Maglogiannis, L. Iliadis, & E. Pimenidis (Eds.), Artificial Intelligence Applications and Innovations, AIAI 2020 IFIP WG 12.5 International Workshops - MHDW 2020 and 5G-PINE 2020, Proceedings (pp. 213-224). (IFIP Advances in Information and Communication Technology; Vol. 585 IFIP). SPRINGER. https://doi.org/10.1007/978-3-030-49190-1_19

Alshammary, Miznah H. ; Iliopoulos, Costas S. ; Mohamed, Manal et al. / Application and Algorithm : Maximal Motif Discovery for Biological Data in a Sliding Window. Artificial Intelligence Applications and Innovations, AIAI 2020 IFIP WG 12.5 International Workshops - MHDW 2020 and 5G-PINE 2020, Proceedings. editor / Ilias Maglogiannis ; Lazaros Iliadis ; Elias Pimenidis. SPRINGER, 2020. pp. 213-224 (IFIP Advances in Information and Communication Technology).

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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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note = "9th 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 ; Conference date: 05-06-2020 Through 07-06-2020",

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Alshammary, MH, Iliopoulos, CS , Mohamed, M & Vayani, F 2020, Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window. in I Maglogiannis, L Iliadis & E Pimenidis (eds), Artificial Intelligence Applications and Innovations, AIAI 2020 IFIP WG 12.5 International Workshops - MHDW 2020 and 5G-PINE 2020, Proceedings. IFIP Advances in Information and Communication Technology, vol. 585 IFIP, SPRINGER, pp. 213-224, 9th 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, 5/06/2020. https://doi.org/10.1007/978-3-030-49190-1_19

Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window. / Alshammary, Miznah H.; Iliopoulos, Costas S.; Mohamed, Manal et al.
Artificial Intelligence Applications and Innovations, AIAI 2020 IFIP WG 12.5 International Workshops - MHDW 2020 and 5G-PINE 2020, Proceedings. ed. / Ilias Maglogiannis; Lazaros Iliadis; Elias Pimenidis. SPRINGER, 2020. p. 213-224 (IFIP Advances in Information and Communication Technology; Vol. 585 IFIP).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

TY - CHAP

T1 - Application and Algorithm

T2 - 9th 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

AU - Alshammary, Miznah H.

AU - Iliopoulos, Costas S.

AU - Mohamed, Manal

AU - Vayani, Fatima

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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.

AB - 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.

KW - Bioinformatics application

KW - Motif discovery

KW - Sequence motifs

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T3 - IFIP Advances in Information and Communication Technology

SP - 213

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BT - Artificial Intelligence Applications and Innovations, AIAI 2020 IFIP WG 12.5 International Workshops - MHDW 2020 and 5G-PINE 2020, Proceedings

A2 - Maglogiannis, Ilias

A2 - Iliadis, Lazaros

A2 - Pimenidis, Elias

PB - SPRINGER

Y2 - 5 June 2020 through 7 June 2020

ER -

Alshammary MH, Iliopoulos CS , Mohamed M , Vayani F. Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window. In Maglogiannis I, Iliadis L, Pimenidis E, editors, Artificial Intelligence Applications and Innovations, AIAI 2020 IFIP WG 12.5 International Workshops - MHDW 2020 and 5G-PINE 2020, Proceedings. SPRINGER. 2020. p. 213-224. (IFIP Advances in Information and Communication Technology). doi: 10.1007/978-3-030-49190-1_19

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

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Keywords

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