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

Overview

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 components:

• selection and optimization of appropriate multiplexed live-cell assays for generating complex datasets
• development and training of an MLA to analyze complex datasets and identify cell response patterns
• evaluation of the performance of the MLA in terms of its ability to accurately predict drug response in live cells.

The undergraduate student will work closely with the biomedical engineering team to gain hands-on experience in multiplexed live-cell assays, MLA, and data analysis techniques.

Basic knowledge of cell biology and biochemistry is preferred but not required. The student will also have the opportunity to contribute to the development of novel multiplexed live-cell assays and participate in data analysis and interpretation.

Research engagement

The undergraduate student will work closely with the biomedical engineering team to gain hands-on experience in multiplexed live-cell assays, MLA, and data analysis techniques.

Research activities

The undergraduate student will work closely with the biomedical engineering team to gain hands-on experience in multiplexed live-cell assays, MLA, and data analysis techniques.

Outcomes

The project will provide valuable insights into the development of MLAs for drug discovery and the application of MLAs in biomedical research. The results of this project may contribute to the development of new drugs and therapeutic approaches for various diseases.

Skills and experience

Students will need to have:

• competence in programming in MATLAB
• experience in programing and software developments.

Start date

1 November, 2024

End date

28 February, 2025

Keywords

• Machine learning algorithm
• biotechnologies
• biomedicine
• tissue cultures
• drug screening
• artificial intelligence
• AI

Contact

Louis.ongjunye@qut.edu.au