Latest News

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.

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.

Congratulations to Kaspar Martens on having his paper accepted in Bioinformatics. Cell type identification plays an important role in the analysis and interpretation of single-cell data and can be carried out via supervised or unsupervised clustering approaches.

Congratulations to Joel Nulsen on having his paper accepted in BMC Bioinformatics. Genomic insights in settings where tumour sample sizes are limited to just hundreds or even tens of cells hold great clinical potential, but also present significant technical challenges.

We continue to contribute to the MUM-PREDICT and OPTIMAL projects over the last six months including: Artificial Intelligence for Multiple Long-term conditions (AIM): A consensus statement from the NIHR AIM consortia

Congratulations to Franziska Gunther on her collaborative work with Breaking Free Online on Identifying factors associated with user retention and outcomes of a digital intervention for substance use disorder: a retrospective analysis of real-world data which has been published in JAMIA Open.

Congratulations to Kaspar Martens on having his paper accepted at MLCB 2023. Generative models for multimodal data permit the identification of latent factors that may be associated with important determinants of observed data heterogeneity.

Congratulations to Franziska Gunther on her collaborative work with Breaking Free Online which was presented at MIE 2023: On the difficulty of predicting engagement with digital interventions for substance use disorders.

The early detection of diseases is a highly desirable approach for addressing conditions where late stage disease cannot be easily treated or managed. Through early detection, it may be possible to take actions to minimise future disease severity or even prevent disease occurrence altogether.

We are proud to have been part of a glittering array of publications arising from our contribution to the MUM-PREDICT projects over the last 12 months. The team also won the Health Data Research UK Team of the Year 2022 award: