The African Institute for Mathematical Sciences (AIMS) believes in the talents and skills of its alumni and students. AIMS feel proud to have hired some of its alumni to work and can attest to their ability to solve different problems using mathematics and technology. In this edition of the #AlumoftheWeek, we bring you the profile of one alumnus who worked at AIMS for several years after completing his studies at AIMS Ghana in 2014. Our alumnus of this week is Degoot Abdoelnaser Mahmod, from Sudan.
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 computational biology research group at AIMS-Rwanda (https://ndifongroup.org/ ). Degoot mainly focuses on mathematics and statistics, and he does efficient programming and parallel computing. He also uses AI as a complementary induction tool for making predictions.
His research interest takes an interdisciplinary shape, encompassing Mathematics, theory of inverse physics, statistical learning and machine intelligence to gain a broad and detailed understanding of biological problems. In his PhD, Degoot 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 achieve prediction accuracies comparable to the state-of-the-art and provide simple and physically meaningful interpretations of the mechanisms underpinning the solutions to the considered problems.
With the emergence of Coronavirus 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. It was successfully implemented by the 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, using mathematical models to study the virus’ local epidemiological curves and estimate the central epidemiological factors that govern its spread. He shared the findings with the relevant health authorities and stockholders, who considered them when making significant 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; he embodied 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.