Congratulations to Ellen Visscher whose paper “Hybrid restricted master problem for Boolean matrix factorisation” was accepted for presentation at AAAI 2026. A preprint paper can be found on arXiv.
This guide reflects my own personal views and opinions and not those of the National Institute for Health and Care Research (NIHR), UK Research and Innovation (UKRI) or the Medical Research Council (MRC). For official information about the funding schemes discussed please refer to the NIHR website.
Congratulations to Hanwen Xing whose paper “Continual learning via probabilistic exchangeable sequence modelling” was accepted in Transactions on Machine Learning Research. A copy of the paper can be found on OpenReview.
Congratulations to Mortiz Gogl whose paper “DoseSurv: Predicting Personalized Survival Outcomes under Continuous-Valued Treatments” was accepted for NeurIPS 2025. A copy of the paper can be found on the NeurIPS25 website.
A preprint of work by Charles Gadd describing “SurvivEHR: a competing risks, time-to-event foundation model for multiple long-term conditions from primary care electronic health records” is now available on medRxiv.
“Multiple long-term conditions (MLTCs) or multimorbidity – the co-occurrence of multiple chronic conditions –presents a growing challenge for primary care. Current predictive models often target single outcomes and overlook the complexities of time-to-event risk in real-world, longitudinal health data. Here, we present SurvivEHR, a generative transformer-based foundation model trained on over 7.6 billion coded events from 23 million patients in UK primary care. SurvivEHR introduces a competing risk time-to-event pretraining objective that enables accurate forecasting of future diagnoses, investigations, medications, and mortality. We demonstrate that SurvivEHR achieves strong risk stratification performance, captures clinically meaningful trajectories, and outperforms benchmark survival models across multiple tasks. The model also transfers effectively to fine-tuned prognostic tasks, particularly in low-resource settings. By learning patient trajectories directly from routine health records, SurvivEHR offers a scalable and privacy-preserving approach for building generalisable clinical risk tools that address the complexity of MLTCs in primary care..”
Congratulations to Hanwen Xing on having his paper “GPerturb: Gaussian process modelling of single-cell perturbation data” accepted in Nature Communications:
“Single-cell RNA sequencing and CRISPR screening enable high-throughput analysis of genetic perturbations at single-cell resolution. Understanding combinatorial perturbation effects is essential but challenging due to data sparsity and complex biological mechanisms. We present GPerturb, a Gaussian process-based sparse perturbation regression model designed to estimate gene-level perturbation effects. GPerturb employs an additive structure to separate signal from noise and captures sparse, interpretable effects from both discrete and continuous responses. It also provides uncertainty estimates for the presence and strength of perturbation effects on individual genes. We demonstrate the use GPerturb on both simulated and real-world datasets, characterising its competitive performance with current state-of-the-art methods. Furthermore, the model reveals meaningful gene-perturbation interactions and identifies effects consistent with known biology. GPerturb offers a novel approach for uncovering complex dependencies between gene expression and perturbations and advancing our understanding of gene regulation at the single-cell level.”
Christopher Yau has contributed as a member of the expert working group to a new report “The Synthetic Data for Development of AI as a Medical Device” now available via PHG Foundation:
Professor Chris Yau will become Co-Director for the new Centre for Doctoral Training in Fundamentals of AI, an exciting new initiative in partnership with EIT Oxford (The Ellison Institute of Technology).
Congratulations to Woojung Kim on having his paper accepted at ML4H 2024.
This paper introduces the Mixed Type Multimorbidity Variational Autoencoder (M3VAE), a deep probabilistic generative model developed for supervised dimensionality reduction in the context of multimorbidity analysis. The model is designed to overcome the limitations of purely supervised or unsupervised approaches in this field. M3VAE focuses on identifying latent representations of mixed-type health-related attributes essential for predicting patient survival outcomes. It integrates datasets with multiple modalities (by which we mean data of multiple types), encompassing health measurements, demographic details, and (potentially censored) survival outcomes. A key feature of M3VAE is its ability to reconstruct latent representations that exhibit clustering patterns, thereby revealing important patterns in disease co-occurrence. This functionality provides insights for understanding and predicting health outcomes. The efficacy of M3VAE has been demonstrated through experiments with both synthetic and real-world electronic health record data, showing its capability in identifying interpretable morbidity groupings related to future survival outcomes. The paper is available on OpenReview.
Congratulations to Charles Gadd on having his paper accepted at MLCB 2024.
Changes in the number of copies of certain parts of the genome, known as copy number alterations (CNAs), due to somatic mutation processes are a hallmark of many cancers. This genomic complexity is known to be associated with poorer outcomes for patients but describing its contribution in detail has been difficult. Copy number alterations can affect large regions spanning whole chromosomes or the entire genome itself but can also be localised to only small segments of the genome and no methods exist that allow this multi-scale nature to be quantified. In this paper, we address this using Wave-LSTM, a signal decomposition approach designed to capture the multi-scale structure of complex whole genome copy number profiles. Using wavelet-based source separation in combination with deep learning-based attention mechanisms. We show that Wave-LSTM can be used to derive multi-scale representations from copy number profiles which can be used to decipher sub-clonal structures from single-cell copy number data and to improve survival prediction performance from patient tumour profiles.