Detecting the Genes that have High Probability of Causing Kawasaki Disease

Bharadwaj, Samradni Ranganath (2024) Detecting the Genes that have High Probability of Causing Kawasaki Disease. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (931kB) | Preview
Preview	PDF (Configuration Manual) Download (608kB) | Preview

Abstract

The present study seeks to gain further insights into infantile Kawasaki disease (KD), a difficult pediatric disease with systemic inflammation that has the potential to affect the coronary arteries. Using current genomic and informatics approaches, the study seeks to discover new genetic determinants of KD susceptibility or risk for severe forms of the disease. Detailed information about the biology of the disease could change how the disease is diagnosed and treated, enabling earlier treatment to increase survival for affected children. These innovations through cutting edge technologies are expected to make a substantial scientific and clinical impact in understanding KD and in the management of clinical care of patients with KD and help reduce the burden of the disease worldwide. KMeans Clustering, Anova, DBSCAN along with PCA and t-SNE were used to identify the gene associated with causing Kawasaki Disease. The PTAFR, PYGL, and APOBEC3G critical genes were highlighted; presenting profound statistical relation with KD. The significant gene associations with Kawasaki Disease, particularly PTAFR (p = 2.80e-23), PYGL (p = 9.08e-28), and APOBEC3G (p = 3.59e-18) which have unique gene sequences that characterize KD patients compared to symptom-free people. These findings identify potential biomarkers for the diagnosis and prognosis of the disease, and, therefore, there is a need for more investigation to determine their roles in the pathogenesis of Kawasaki Disease and in the clinical setting.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Mulwa, Catherine UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > Healthcare Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	07 Aug 2025 13:54
Last Modified:	07 Aug 2025 13:54
URI:	https://norma.ncirl.ie/id/eprint/8465

Actions (login required)

View Item