Webinar: PRIME Workshop - How AI/ML methods can enhance ME/CFS molecular or genetic biomarker discovery, Jan 21, 2026, 02:00 to 05:00 PM (GMT)

[Andy]

Registration link

General abstract:

ME/CFS and Long Covid (LC) are chronic and debilitating illnesses whose symptoms often persist years after disease onset. LC occurs after SARS-CoV-2 viral infection and ME/CFS often occurs after other infections. ME/CFS is defined by post-exertional malaise (PEM), the dramatic worsening of symptoms, or new symptoms, after even minor mental or physical exertion. ME/CFS and LC share substantial overlap in symptomatology.

There are no medical interventions that cure ME/CFS and LC. However, data sources including large-scale Biobanks (e.g., UK Biobank, All-of-Us), Electronic Health Records and clinical trials are available to investigate questions regarding diagnosis or disease trajectories, and to generate hypotheses for disease-causal molecular pathways to allow design of possible treatments.

Rigorous AI/ML and statistical methodologies play a central role in answering such questions. The aim of this workshop is to present state-of-the-art quantitative methodologies that have been, or could be, applied in the context of ME/CFS and LC to motivate and empower researchers to take on and apply promising techniques in their own work in this area.

The workshop covers key considerations for valid application of advanced methodologies, explains common mistakes when applying the methods (e.g., model-misspecification in estimation problems, lack of held-out data for final evaluation of prediction model, lack of uncertainty quantification), and presents exemplar biomedical results.

Speakers:

• Nima Hejazi – Assistant Professor of Biostatistics at Harvard T.H. Chan School of Public Health) • Jiabou Xu – Lecturer in Biomedical Engineering, University of Glasgow

• Philippe Boileau – Assistant Professor of Biostatistics, McGill University

• Maria Delgado Ortet – Cross-Disciplinary Postdoctoral Fellow, University of Edinburgh • Nuno Sepulveda – Head of Immune-Stats Group at Warsaw University of Technology



Programme: Talks 20mins + 10mins questions

2-2.05pm Welcome from the Chair (Dr. Sjoerd Beentjes)

2.05-2.10pm Introduction to PRIME – building infrastructure for Patients, Researchers & Industry for ME/CFS – Prof. Chris Ponting

2.10-2.40pm Talk 1: Evaluating the impact of SARS-CoV-2 infection and Long COVID on health outcomes measured via tiered sampling in RECOVER-Adult - Nima Hejazi, Harvard T. H. Chan School of Public Health

2.40-3.10pm Talk 2: Single-Cell Diagnostics of ME/CFS and Autoimmune Diseases - Jiabao Xu, University of Glasgow

3.10-3.40pm Talk 3: Detecting Heterogeneous Treatment Effects in Clinical Trials with Differential Variance Methods - Philippe Boileau, McGill University

3.40-3.50pm - BREAK

Chair (Dr. Ava Khamseh)

3.50-4.20pm Talk 4: Conformal Prediction and Subgroup Stratification in ME/CFS Blood Biomarkers - Maria Delgado-Ortet, University of Edinburgh

4.20-4.50pm Talk 5: Prevalence estimation of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome among long COVID patients using symptoms’ aggregated data - Nuno Sepulveda, Warsaw University of Technology

4.50-5.00pm Concluding Remarks

Registration link

This will be recorded and made available to view by all in the days after the event. Flyer attached to this post in PDF format.

 

[Andy]

Speaker Bios:

Nima Hejazi is an assistant professor of biostatistics at the Harvard Chan School of Public Health. His research interests primarily center on causal inference, semi-parametric estimation and causal machine learning, assumption-lean and non-parametric inference, statistical machine learning, and computational statistics. His methodological work is usually motivated by applied science investigations in the infectious disease sciences, the study of chronic diseases, and cancer science. He is also interested in open-source software and high-performance computing for the statistical sciences—to push the boundaries of statistical methodology and to promote transparency and reproducibility in the practice of applied statistics and statistical data science.

Jiabao Xu is a Lecturer in Biomedical Engineering at the University of Glasgow, where she leads a research group developing AI-enhanced single-cell technologies for disease diagnostics. She combines Raman spectroscopy, micro-engineering, and machine learning to map immune dysfunction across complex conditions including ME/CFS and autoimmune disease. Her work spans fundamental discovery to clinical translation through large clinical cohorts and international collaborations.

Philippe Boileau, PhD is an Assistant Professor of Biostatistics at McGill University. He is broadly interested in the development of assumption-lean statistical methods and their application to quantitative problems in the health and life sciences. Assumption-lean methods combine causal inference and machine learning techniques to avoid unjustified assumptions about how data are generated, encouraging robust statistical inference. Dr. Boileau’s recent work has focused on creating assumption-lean methods for heterogeneous treatment effect discovery in clinical trial data

Maria Delgado-Ortet is a Cross-Disciplinary Post-Doctoral Fellow at the Institute of Genetics and Cancer at the University of Edinburgh. Her research focuses on computational and statistical methods for biomedical data, particularly prediction modelling and uncertainty quantification. She specialises in applying conformal prediction to improve the reliability of biomarker analyses and contributes to a collaborative effort investigating ME/CFS blood biomarker models, including calibrated prediction and exploratory subgroup characterisation. She works closely with quantitative and biological research teams to develop robust, interpretable computational methods for challenging biomedical datasets, including ME/CFS biomarkers.

Nuno Sepulveda has a bachelor and master degree in Applied Mathematics & Computation from the Technical University of Lisbon. He has a PhD degree in Biomedical Sciences from the University of Oporto. He has more 20 years of experience working on applied statistics, genetics, genomics, epidemiology and public health of tropical infectious diseases with special focus on malaria. He currently investigates myalgic encephalomyelitis/chronic fatigue syndrome and long covid using serological data via machine and statistical learning methods.