Fourth Meeting of the DS-I Africa Consortium
16 - 22 November 2024
The Ravenala Attitude Hotel, Mauritius

Dr Samuel Kyobe - Participant

Utilizing Data Science to Predict and Improve Health Outcomes in Pediatric HIV (MPI)

I am medical doctor and medical microbiologist with doctoral training in human genetics and bioinformatics. My long-term research interest is to identify novel host genetic factors that can be explored to understand the molecular mechanisms of diseases, and how these factors can be explored to design vaccines and therapeutics. I have experiential training in molecular biology with essential skills in molecular biology and immunology. My research , I aim to employ machine learning to integrate temporal Electronic Health Records (EHRs) with genomic data for predicting the longitudinal risk of Metabolic Syndrome (MetS) in African children on life-long antiretroviral therapy (ART). This involves generating predictive models for the longitudinal risk and trajectory of MetS and discovering novel genes and biomarkers associated with dolutegravir-induced MetS in diverse African populations. Furthermore, I intend to develop composite risk scores to predict MetS risk, informing the future development of targeted health interventions for different risk groups among HIV-infected children in Africa. Additionally, my research focuses on using explainable machine learning algorithms to integrate multi-omics data with temporal EHRs for enhancing the diagnosis of active Tuberculosis (TB) in HIV-infected children. This includes applying neural network algorithms to identify molecular signatures of TB, with the ultimate goal