Computational prediction of treatment outcome by machine learning
Principal Investigator
Name
Matloob Khushi
Degrees
Ph.D
Institution
School of Computer Science
Position Title
Director, Master of Data Science School of Computer Science
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-496
Initial CDAS Request Approval
Jul 25, 2019
Title
Computational prediction of treatment outcome by machine learning
Summary
Machine learning has been successfully applied to solve many biological problems and undertake drug discovery. In this project, we aim to apply the techniques to better predict the treatment plan for patients. The patient data will be collected from disease-specific banks. There are many data collection biobanks in Australia that collect patients’ clinical history, treatment, and other important data such as age at diagnosis, disease type, hormonal status, lifestyle factors, and pathology test results. These banks also follow up patients for a certain number of years to record the health status and progression of their disease. Existing machine learning classifier algorithms will be evaluated and novel methods will be investigated to predict the best possible treatment.
Aims
1. Develop pipelines for data cleaning and preprocessing
2. Find out what might be the best ML algorithm for prediction of cancer.
3. Achieving better prediction than other similar studies.
Collaborators
Dr Matloob Khushi