Feature attributions based on the Shapley value are popular for explaining machine
learning models. However, their estimation is complex from both theoretical and …

Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver
disease. The management of LT recipients is complex, predominantly because of the need …

Brain clocks, which quantify discrepancies between brain age and chronological age, hold
promise for understanding brain health and disease. However, the impact of diversity …

This study investigates the efficacy of Explainable Artificial Intelligence (XAI) methods,
specifically Gradient-weighted Class Activation Map** (Grad-CAM) and Shapley Additive …

Population genetics is transitioning into a data-driven discipline thanks to the availability of
large-scale genomic data and the need to study increasingly complex evolutionary …

Heavy metal exposure is a common risk factor for hypertension. To develop an interpretable
predictive machine learning (ML) model for hypertension based on levels of heavy metal …

Tropical cyclone rainfall (TCR) extensively affects coastal communities, primarily through
inland flooding. The impact of global climate changes on TCR is complex and debatable …

The rapid development of machine learning (ML) techniques has opened up the data-dense
field of microbiome research for novel therapeutic, diagnostic, and prognostic applications …

Rapid, reliable and accurate interpretation of medical time series signals is crucial for high-
stakes clinical decision-making. Deep learning methods offered unprecedented …

Background Biological age is a measure of health that offers insights into ageing. The
existing age clocks, although valuable, often trade off accuracy and interpretability. We …