A Scalable Algorithm for Interpreting DNA Sequence and Predicting the Response of Killer T-Cells in Systemic Lupus Erythematosus Patients
Journal Title: International Journal of Bioinformatics and Intelligent Computing - Year 2022, Vol 1, Issue 1
Abstract
The incidence and prevalence of SLE in North America are 23.2 and 241 per 100,000 people per year respectively while the incidence in Africa is 0.3 per 100,000 people per year. The study aims to predict the autoimmune response of killer T-cells in a patient suffering from Systemic Lupus Erythematosus by searching for variations in genes regulating the activities of Killer T cells. An approximate matching algorithm applying the Boyer-Moore Algorithm for the matching algorithm. Nucleotide sequences of each of the genes liked to Killer T-cells in reference human genome to DNA sequences of SLE patients. The threshold on all single nucleotide polymorphisms (SNPs) is set to 10% of the nucleotide sequence length of the gene. For 50% of susceptibility genes with no match the patient is susceptible. Sixteen (16) patients show that they are all guaranteed to manifest autoimmune Killer T-cells. The algorithm can predict the response of killer T-cells and improve the early detection and treatment of SLE patients. A similar approach can be used for genetically linked diseases like cancer.
Authors and Affiliations
Wai Lok Woo, Nwoye O Ephraim, Fidelis P Obinna, Nwosu O I, Balogun O Jessy, Raid Rafi Al-Nima
Keywords
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- EP ID EP724393
- DOI https://doi.org/10.61797/ijbic.v1i1.141
- Views 70
- Downloads 0
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