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Development of a machine learning algorithm for high throughput cell response data in drug therapy

High-throughput screening assays are essential for accelerating drug discovery, but current assays often rely on endpoint measurements that do not capture the dynamic response of cells to drug treatment. Machine learning algorithms (MLAs) have the potential to enable real-time, high-throughput monitoring of cell response to drug treatment by analyzing complex datasets generated by multiplexed live-cell assays. This research project aims to develop an MLA for enabling high throughput cell response data in drug treatment. The project will involve three main …

Study level
Honours
Faculty
Faculty of Engineering
School
School of Computer Science
Research centre(s)
Centre for Biomedical Technologies
Centre for Biomedical Technologies

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