Tackling underrepresentation in STEM
In Summer 2022, we joined Health Data Research UK’s Black Internship programme.
The initiative is aimed at Black people who are recent graduates or are completing their undergraduate degree. The programme aims to tackle underrepresentation of Black people within science, technology, engineering, and mathematics careers.
Shannon joined us this summer as an intern at Barts Life Sciences and shares her experience with the programme, and her work.
"Hello, my name is Shannon, and I was an intern at Barts Life Sciences over the summer as part of the Black Internship Programme organised by Health Data Research UK (HDR UK).
I remained eager to increase my knowledge
"My Master’s degree in biochemistry, together with my interest in innovative research, has provided me with several opportunities to complete exciting computational and wet lab projects. Through these experiences, I have learnt a lot about the mechanisms which result in the maintenance of health conditions and the development of diseases, and about how this knowledge can be harnessed for the identification of therapeutic drug targets.
"I remained eager to increase my knowledge of the different treatments available to target diseases, and to directly support clinical research. The data science internship was the perfect way to achieve these goals, due to the opportunity to employ machine learning and artificial intelligence algorithms to help develop the approaches which affect patient care.
My hopes for this internship were fulfilled
"As I reflect on my experience at Barts, I can confidently say that my hopes for this internship were fulfilled. My project aimed to identify what factors might affect the choice of treatment that’s given to children with inflammatory bowel disease, using data from an existing study. To do this, I used data science and machine modelling, and a whole lot of statics (think logistic regression, random forest and artificial neural network algorithms in R!).
"I then looked at the impact of adding one specific factor, or combination of factors, to the model to see if it made it better at determining if a treatment was given or not, and how it worked. This allowed me to gain experience with a range of algorithms and provided the chance to compare which factors could affect a particular outcome between the models generated by different algorithms. In other words, it allowed me to better understand which treatments work better for which patients and how they work.
This could help researchers and clinicans accurate evaluate the efficacy of treatments
"Long-term, the results from this work and my model could help researchers and doctors to accurately evaluate the how well treatments given to children with inflammatory bowel disease work. This is because, sometimes the observed differences between patients receiving different treatments (in terms of their health) may be associated with the fact that specific variables influence the type of treatment received, yet these differences may be mistakenly interpreted as a reflection of how effective the treatments are.
I am thankful to Barts Life Sciences and HDR UK for providing me with this internship
"I thoroughly enjoyed the process of refining my critical thinking skills whilst gaining a deeper understanding of how data science can be used to facilitate our clinical understanding and, consequently, I hope to continue to work with my supervisor to further develop my project. Regardless, I am thankful to Barts Life Sciences and HDR UK for providing me with this internship, and I am very grateful to have learnt so much in the past 8 weeks."