Determining Optimal Hierarchical Clustering by Combining Needleman Wunsch and Jukes Cantor Algorithms in Tuberculosis (TB) Disease Clustering
Year: 2025
Type: article
Abstract: Tuberculosis (TBC) is an infectious disease affecting the respiratory system, caused by the bacterium Mycobacterium tuberculosis. Tuberculosis (TBC) remains a global concern, and to date, no country is completely free from TB. This disease continues to be one of the leading causes of mortality. Therefore, it is essential to categorize the spread of TBC. The percentage of identity in genetic codes will reveal the proportion of mutations. The percentage of identity in genetic codes will demonstrate that, although the symptoms caused by a disease may be quite similar, the protein sequences are not necessarily the same. In this study, the researchers employed the Hierarchical Clustering method, integrating the Needleman-Wunsch and Jukes-Cantor algorithms, resulting in two groups. The first group consists of 9 interconnected rows, while the second group consists of 7 interconnected rows.
Authors: Hildatul Anizah, Tony Yulianto, Kuzairi, Ira Yudistira, Rica Amalia
Institution: Universitas Islam Madura
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410241193