Aparajita Karmakar

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PhD Student

Thesis Title: Using Machine Learning to Predict siRNA Gene Expression Knockdown Efficacy

Primary Theme: Artificial Intelligence and Informatics

Secondary Themes: Structural Biology and Next Generation Chemistry

Franklin Supervisors: Dr Alex Lubbock, Dr Gwyndaf Evans, Dr Angus Weir

University: University of Edinburgh

University Supervisors: Dr Grzegorz Kudla, Dr David Clarke

External Supervisors: Abdulhamid Merii (Silence Therapeutics), Dr Russell Sutherland (Silence Therapeutics)

Small interfering RNAs (siRNAs) are a promising technology for targeted gene silencing therapeutics across a wide range of genetic ailments, but designing highly effective siRNAs for strong target inhibition and low toxicity remains challenging. Aparajita’s work focuses on building AI-based computational models to predict siRNA knockdown efficacy, incorporating factors including RNA structure, chemical modifications, and target gene transcripts. If successful, this work will optimise in vitro production of siRNA to maximise therapeutic benefit to patients of new siRNA therapies whilst minimising off target effects, production time, and development costs.

Aparajita has completed her Bachelor’s degree in Computer Science and Master’s degree in Data Science. Throughout her academic journey, she was engaged in the application of AI and image analysis algorithms for medical diagnostics.