The researcher's work centers on advancing computational approaches to predict protein function through interdisciplinary research combining computer science with biological sciences. Methods such as graph algorithms and tensor networks are employed within the framework of quantum computing for solving intricate equations relevant to protein structure. Additionally, machine learning techniques like deep learning are applied to enhance predictions in brain-computer interfaces and other complex biological problems. The integration of graph theory and reinforcement learning further elevates their approach, enabling more accurate and efficient function prediction across diverse biological contexts.
All Papers
No papers found for the selected criteria.
No collaborations found in the dataset.
This profile is generated from publicly available publication metadata and is intended for research discovery purposes. Themes, summaries, and trajectories are inferred computationally and may not capture the full scope of the lecturer's work. For authoritative information, please refer to the official KNUST profile.