Monday Lunch Live
Hypothesis-free discovery of cancer predictors using machine learning
12 August 2024
Can lifestyle factors, personal traits, and clinical biomarkers help identify those at risk of developing cancer? This webinar will explore how machine learning uncovers key predictors of cancer in general, and specifically ovarian cancer, by analysing thousands of characteristics for each individual within a large population.
Combining machine learning and conventional statistical approaches to identify risk factors for overall and ovarian cancer in a large cohort study
Machine learning can significantly aid in identifying risk factors from large biomedical datasets. In this webinar, we discuss how we integrate machine learning with conventional epidemiological methods to identify adverse and protective risk factors for disease outcomes. We present results from our study on predicting risk factors for cancer. Following this, we share findings from a subsequent study conducted in collaboration with expert gynaecological oncologists and consumer members, aimed at discovering risk factors for ovarian cancer to enable earlier detection and inform new prevention strategies for this cancer, which currently has a poor prognosis due to late-stage diagnosis.
Chair
A/Prof An Duy Tran
Principal Research Fellow, Melbourne School of Population and Global Health
Associate Professor An Duy Tran is co-Head of the Health Economics and Simulation Modelling for Chronic Disease Unit at the Centre for Health Policy, Melbourne School of Population and Global Health. His current research focuses on economic evaluations of health care programs and development of outcomes simulation models for chronic diseases including diabetes, cardiovascular disease, chronic kidney disease and rheumatic diseases. He is also Head of the Health Economics Node within the Methods and Implementation Support for Clinical and Health Research (MISCH) Hub at the University of Melbourne, and co-Lead of the Health Economics Platform of the Australian Centre for Accelerating Diabetes Innovations (ACADI).
Dr Iqbal Madakkatel
Dr Madakkatel is a Research Associate in the Nutritional and Genetic Epidemiology Research Group, specialising in the application of Machine Learning in Epidemiology and Public Health. His research focuses on feature selection and risk factor discovery, aiming to uncover critical insights that can inform public health interventions. He is passionate about both developing new methodologies and applying existing ones to extract valuable information from health data.
Having completed his Diploma and Bachelor’s degree in Computer Engineering, he pursued further education and received his M.Sc. degree in Information Technology, with a focus on Informatics. In 2021, he obtained a PhD in Data Science with a focus on Epidemiology/Public Health.
Dr Amanda Lumsden
From a background in molecular biology research (PhD in Genetics and postdoctoral experience in genetics/physiology), Amanda transitioned from the ‘wet lab’ to join Professor Elina Hyppönen's Nutritional and Genetic Epidemiology Research Group, where she works on large cohort data projects identifying risk factors for conditions including cancer.
As a Research Fellow, and Project Manager of an MRFF-funded ovarian cancer project, Dr Lumsden works closely with consumer members, researchers, and gynaecological oncologists, and is passionate about finding ways to identify women at risk of ovarian cancer to help facilitate earlier detection, and discovering new risk factors that can inform on strategies to prevent its incidence.
Resource details
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