Abdoelnaser M. Degoot is an AIMS-Canada research associate at the African Institute for Mathematical Sciences (AIMS) and a member of Prof Wilfred Ndifon’s research group of computational biology at AIMS-Rwanda (https://ndifongroup.org/). Although he relies on both mathematics and statistics, he also does efficient programming, parallel computing and uses AI as complementary tools of induction and for making predictions.
His research interest takes an interdisciplinary shape, encompassing Mathematics, theory of inverse physics, statistical learning and machine intelligence to gain broad and detailed understanding of biological problems. In his PhD work, he developed mathematical models based on inverse statistics and statistical learning for two machine learning problems in biology: prediction of peptide-MHC-II interactions and antigenic similarity between influenza viruses. He showed that these approaches not only achieve prediction accuracies comparable to the state-of-the-art but also provide simple and physically meaningful interpretations of the mechanisms underpinning the solutions to the considered problems.
With the emergence of Corona-virus disease in the last two years, he has been working on several initiatives to control and mitigate the virus. He worked on the further development, scaling-up, and automation of a group testing algorithm developed by Prof Wilfred Ndifon (AIMS’s Chief Scientific Officer) and Professor Neil Turok (the founder of AIMS). The algorithm enables mass surveillance at significantly reduced cost and resources, and it was successfully implemented by Rwanda Biomedical Center, which is one of the reasons why the country ranked among the top seven in the world in response to this deadly disease. He also worked on the COVID-19 task forces in Sudan and Rwanda, where mathematical models were used to study the virus’ local epidemiological curves and estimated the central epidemiological factors that govern its spread. Findings were shared with the relevant health authorities and stockholders, who then considered them when making major decisions such as imposing new measures or lifting existing ones.
Degoot considers himself a complete product of AIMS. For the past ten years, he has been a student, a tutor, a lecturer, a supervisor, and a researcher at AIMS and an embodiment of its vision as he embarks on his career in solving real-world problems, armed with a set of skills and knowledge he gained at AIMS combined with his creativity.
Below are some of his publications
1. Degoot AM, Chirove F and Ndifon W (2018) Trans-Allelic Model for Prediction of Peptide:MHC-II Interactions. Front. Immunol. 9:1410. doi:10.3389/fimmu.2018.01410.
2. Degoot AM, Adabor, Emmanuel S., Faraimunashe Chirove, and Wilfred Ndifon (2019) A Simple Model for Predicting Antigenicity of Influenza A Viruses based on biophysical ideas. Scientific Reports, vol 9(1). doi: https://doi.org/10.1038/s41598-019-46740-5.
3. Degoot AM, Wilfred Ndifon and Faraimunashe Chirove. A Biophysical Model for Prediction of Peptide:HLA-DR Molecules Interactions Based on Genomic and Structural Data. BMC Bioinformatics (under revision).
4. Elsheikh, Sara & Abbas, Mohamed & Bakheet, Mohamed & Degoot, Abdo. (2020). A Mathematical Model for the Transmission of CoronaVirus Disease (COVID-19) in Sudan. 10.13140/RG.2.2.24167.27043/1.
5. Degoot AM, and Wilfred Ndifon (2022) AMHCgan: MHC-I Binding Peptides Generator. Submitted to the journal of Immunoinformatics (IMMUNO-D-21-00021), under review.