26 May 2022
Zoom
5-6pm
On Thursday, May 26, at 5pm, our MSc programme will have the pleasure and honor of hosting online Dr. Konstantinos Sechidis, Associate Director of Data Science at Novartis, who will give an invited lecture on “Quantifying uncertainty on machine learning-based predictive biomarker discovery”.
Zoom link: https://authgr.zoom.us/j/95827298539?pwd=VzZFZHp0ckJMVTJGbkppeXVVVjJjUT09
Abstract: One of the key challenges of personalized medicine is to identify which patients will respond positively to a given treatment. The area of subgroup identification focuses on this challenge, that is, identifying groups of patients that experience desirable characteristics, such as an enhanced treatment effect. A crucial first step towards the subgroup identification is to identify the baseline variables (eg, biomarkers) that influence the treatment effect, which is known as predictive biomarkers. When we discover predictive biomarkers it is crucial to have control over the false-positives to avoid waste of resources, as well as provide guarantees over the replicability of our findings. With our work we introduce a set of methods for controlled predictive biomarker discovery, and we use them to explore heterogeneity in psoriatic arthritis trials. The associated paper can be found at https://onlinelibrary.wiley.com/doi/10.1002/sim.9134
Bio: Kostas is an Associate Director of Data Science in Novartis’ Advanced Exploratory Analytics group. His main areas of interest are machine learning based biomarker discovery, subgroup identification, and development of digital endpoints. He obtained his PhD in statistical machine learning from the Department of Computer Science of the University of Manchester. Afterwards he spent many years as post-doctoral researcher on developing novel methodologies for analysing: self-reported epidemiological data with Manchester’s Health e-Research Center, clinical trials data for personalised medicine with AstraZeneca and digital healthcare data for digital biomarker development with Roche. He is member of the editorial board of Machine Learning and more information about his work can be found at: https://sechidis.netlify.app/