Document Type
Postprint
Publication Date
1-1-2022
Journal / Book Title
Proceedings 2022 IEEE International Conference on Big Data Big Data 2022
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.
DOI
10.1109/BigData55660.2022.10020454
Montclair State University Digital Commons Citation
Hidalgo, Rafael; Devito, Anthony; Salah, Nesreen; Varde, Aparna S.; and Meredith, Robert W., "Inferring Phylogenetic Relationships using the Smith-Waterman Algorithm and Hierarchical Clustering" (2022). Department of Biology Faculty Scholarship and Creative Works. 554.
https://digitalcommons.montclair.edu/biology-facpubs/554