Inferring Phylogenetic Relationships using the Smith-Waterman Algorithm and Hierarchical Clustering

Rafael Hidalgo; Anthony Devito; Nesreen Salah; Aparna S. Varde; Robert W. Meredith

doi:10.1109/BigData55660.2022.10020454

Inferring Phylogenetic Relationships using the Smith-Waterman Algorithm and Hierarchical Clustering

Rafael Hidalgo, Anthony Devito, Nesreen Salah, Aparna S. Varde, Robert W. Meredith

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

2 Scopus citations

Abstract

All biological species undergo change over time due to the evolutionary process. These changes can occur rapidly and unpredictably. Due to their high potential to spread quickly, it is critical to be able to monitor changes and detect viral variants. Phylogenetic trees serve as good methods to study evolutionary relationships. Complex big data in biomedicine is plentiful in regards to viral data. In this paper, we analyze phylogenetic trees with reference to viruses and conduct dynamic programming using the Smith-Waterman algorithm, followed by hierarchical clustering. This methodology constitutes an intelligent approach for data mining, paving the way for examining variations in SARS-Cov-2, which in turn can help to discover knowledge potentially useful in biomedicine.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
Editors	Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5910-5914
Number of pages	5
ISBN (Electronic)	9781665480451
DOIs	https://doi.org/10.1109/BigData55660.2022.10020454
State	Published - 2022
Event	2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan Duration: 17 Dec 2022 → 20 Dec 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference	2022 IEEE International Conference on Big Data, Big Data 2022
Country/Territory	Japan
City	Osaka
Period	17/12/22 → 20/12/22

Keywords

Algorithms
SARS-Cov-2
biological data
clustering
data analytics
healthcare
intelligent data mining
knowledge discovery
medicine
phylogeny
virology
virus

Access to Document

10.1109/BigData55660.2022.10020454

Cite this

Hidalgo, R., Devito, A., Salah, N., Varde, A. S., & Meredith, R. W. (2022). Inferring Phylogenetic Relationships using the Smith-Waterman Algorithm and Hierarchical Clustering. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (pp. 5910-5914). (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData55660.2022.10020454

Hidalgo, Rafael ; Devito, Anthony ; Salah, Nesreen et al. / Inferring Phylogenetic Relationships using the Smith-Waterman Algorithm and Hierarchical Clustering. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. editor / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 5910-5914 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

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title = "Inferring Phylogenetic Relationships using the Smith-Waterman Algorithm and Hierarchical Clustering",

abstract = "All biological species undergo change over time due to the evolutionary process. These changes can occur rapidly and unpredictably. Due to their high potential to spread quickly, it is critical to be able to monitor changes and detect viral variants. Phylogenetic trees serve as good methods to study evolutionary relationships. Complex big data in biomedicine is plentiful in regards to viral data. In this paper, we analyze phylogenetic trees with reference to viruses and conduct dynamic programming using the Smith-Waterman algorithm, followed by hierarchical clustering. This methodology constitutes an intelligent approach for data mining, paving the way for examining variations in SARS-Cov-2, which in turn can help to discover knowledge potentially useful in biomedicine.",

keywords = "Algorithms, SARS-Cov-2, biological data, clustering, data analytics, healthcare, intelligent data mining, knowledge discovery, medicine, phylogeny, virology, virus",

author = "Rafael Hidalgo and Anthony Devito and Nesreen Salah and Varde, {Aparna S.} and Meredith, {Robert W.}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",

year = "2022",

doi = "10.1109/BigData55660.2022.10020454",

language = "English",

series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "5910--5914",

editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",

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Hidalgo, R, Devito, A, Salah, N, Varde, AS & Meredith, RW 2022, Inferring Phylogenetic Relationships using the Smith-Waterman Algorithm and Hierarchical Clustering. in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, Institute of Electrical and Electronics Engineers Inc., pp. 5910-5914, 2022 IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, 17/12/22. https://doi.org/10.1109/BigData55660.2022.10020454

Inferring Phylogenetic Relationships using the Smith-Waterman Algorithm and Hierarchical Clustering. / Hidalgo, Rafael; Devito, Anthony; Salah, Nesreen et al.
Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. ed. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. p. 5910-5914 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

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

TY - GEN

T1 - Inferring Phylogenetic Relationships using the Smith-Waterman Algorithm and Hierarchical Clustering

AU - Hidalgo, Rafael

AU - Devito, Anthony

AU - Salah, Nesreen

AU - Varde, Aparna S.

AU - Meredith, Robert W.

PY - 2022

Y1 - 2022

N2 - All biological species undergo change over time due to the evolutionary process. These changes can occur rapidly and unpredictably. Due to their high potential to spread quickly, it is critical to be able to monitor changes and detect viral variants. Phylogenetic trees serve as good methods to study evolutionary relationships. Complex big data in biomedicine is plentiful in regards to viral data. In this paper, we analyze phylogenetic trees with reference to viruses and conduct dynamic programming using the Smith-Waterman algorithm, followed by hierarchical clustering. This methodology constitutes an intelligent approach for data mining, paving the way for examining variations in SARS-Cov-2, which in turn can help to discover knowledge potentially useful in biomedicine.

AB - All biological species undergo change over time due to the evolutionary process. These changes can occur rapidly and unpredictably. Due to their high potential to spread quickly, it is critical to be able to monitor changes and detect viral variants. Phylogenetic trees serve as good methods to study evolutionary relationships. Complex big data in biomedicine is plentiful in regards to viral data. In this paper, we analyze phylogenetic trees with reference to viruses and conduct dynamic programming using the Smith-Waterman algorithm, followed by hierarchical clustering. This methodology constitutes an intelligent approach for data mining, paving the way for examining variations in SARS-Cov-2, which in turn can help to discover knowledge potentially useful in biomedicine.

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KW - SARS-Cov-2

KW - biological data

KW - clustering

KW - data analytics

KW - healthcare

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KW - medicine

KW - phylogeny

KW - virology

KW - virus

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DO - 10.1109/BigData55660.2022.10020454

M3 - Conference contribution

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T3 - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

SP - 5910

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BT - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

A2 - Tsumoto, Shusaku

A2 - Ohsawa, Yukio

A2 - Chen, Lei

A2 - Van den Poel, Dirk

A2 - Hu, Xiaohua

A2 - Motomura, Yoichi

A2 - Takagi, Takuya

A2 - Wu, Lingfei

A2 - Xie, Ying

A2 - Abe, Akihiro

A2 - Raghavan, Vijay

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Conference on Big Data, Big Data 2022

Y2 - 17 December 2022 through 20 December 2022

ER -

Hidalgo R, Devito A, Salah N, Varde AS , Meredith RW. Inferring Phylogenetic Relationships using the Smith-Waterman Algorithm and Hierarchical Clustering. In Tsumoto S, Ohsawa Y, Chen L, Van den Poel D, Hu X, Motomura Y, Takagi T, Wu L, Xie Y, Abe A, Raghavan V, editors, Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 5910-5914. (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). doi: 10.1109/BigData55660.2022.10020454

Inferring Phylogenetic Relationships using the Smith-Waterman Algorithm and Hierarchical Clustering

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