Machine Learning
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.
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.”
