MRC Biostatistics Unit

I’m a researcher at the MRC Biostatistics Unit, University of Cambridge.

I’m interested in many areas across statistics, machine learning, and their interactions. My research focuses on tailored model development, that is targeted model building for a specific task of interest, mostly prediction.

I’m also developing and applying tools leveraging genomics to detect cancer earlier, which will ultimately lead to more personalized treatment for patients. Toward these goals, I draw from a wide range of disciplines, including molecular biology, computational biology, and medical oncology. More specifically, I’m looking at novel ways to incorporate liquid biopsies into the management of cancer. Liquid biopsies - the analysis of tumours using biomarkers circulating in fluids such as the blood - have the potential to change the way cancer is diagnosed, monitored, and treated.

- Bayesian Methods
- Biomedical Research
- Statistical Computing
- Statistical Modelling
- Longitudinal Data Analysis
- Statistical Learning
- Machine Learning
- Medical Decision Making
- Predictive Modelling and Model Validation
- Survival Analysis
- Translational Genomics

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Regional Histopathology and Prostate MRI Positivity: A Secondary Analysis of the PROMIS Trial.
In *Radiology*.

(2022).
(2021).
False Positive Multiparametric Magnetic Resonance Imaging Phenotypes in the Biopsy-naïve Prostate: Are They Distinct from Significant Cancer-associated Lesions? Lessons from PROMIS.
In *EUROPEAN UROLOGY*.

(2021).
Development and External Validation of Prediction Models for 10-Year Survival of Invasive Breast Cancer. Comparison with PREDICT and CancerMath.
In *Clinical Cancer Research*.

(2018).
Ventilatory limitation and dynamic hyperinflation during exercise testing in Cystic Fibrosis.
In *Pediatric pulmonology*, 52(1), 29-33.

(2017).
Lecturer (2022-2023) at the MPhil in Computational Biology, University of Cambridge.

Dissertation Supervisor (2022-2023) at the MPhil in Computational Biology.

Teaching Assistant and Dissertation Supervisor (2021-2022) at the MPhil in Population Health Sciences, University of Cambridge.

Supervisor Statistics IB, Lent 2019 (2nd year undergraduate course from the Department of Pure Mathematics and Mathematical Statistics, University of Cambridge).

This is the handbook for the course I teach as a Brilliant club tutor. The Brilliant Club is a charity aiming to increase the number of pupils from under-represented backgrounds progressing to highly selective universities.

The material is designed for Key stage 4 pupils but can be easily adapted to Key stage 5.

Cite this work as: Solon Karapanagiotis. Which bicycle lock should I buy? A journey to decision making under uncertainty, 2019.

Statistics · R · Random

Puzzle #224 from the New Scientist is:
I collect Russian dolls, the type where each doll can be opened to reveal a smaller one inside. I am particularly fond of my simple, single-coloured ones, which come in sets of five (and, unusually, have a hollow smallest doll).

The article by Matsuo et al. (2019) appeared in my newsletter. It is another attempt to sell deep-learning (DL) as a promising alternative to traditional survival analysis. To this end, they compare the performance of their DL model to Cox proportional hazard regression (CPH) when predicting survival for women with newly diagnosed cervical cancer.