Detailed tracking of live predators attacking live schools of prey reveals the crucial interplay between predator attack strategy and prey behaviour and positioning in determining the predation risk within mobile animal groups.

Clinical prediction rules could help identify children at risk of slowed growth after an episode of diarrheal illness, and these rules may be generalizable to all children, regardless of diarrhea status.

Computational methods were used to develop accurate manual scorecards for early detection of participants at risk of type 2 diabetes based on the UK Biobank database.

Integrating machine learning (ML) models with selected features improved the prediction accuracy of diabetic kidney disease compared to traditional logistic regression models using established features, revealing potential new markers and supporting the use of ML techniques in epidemiological studies.

A new software package provides more accurate cancer risk prediction profiles and has the ability to integrate more genes and cancer types in the future.

Site-specific polygenic risk scores can stratify the risk of developing breast, prostate, and colorectal cancers in this East Asian population.

Using data for 2566 COVID-19 patients from five hospitals, models are developed to predict for each patient hospitalization and critical care needs, based on demographics, comorbidities, medications, and laboratory findings.

The generalizability of CODOP in distinct world regions and its flexibility to reckon with the changing availability of hospital resources makes it a clinically useful tool potentially improving the outcome prediction and the management of COVID-19 hospitalized patients.

A developed and validated nomogram better predicts the risk of hypertension based on a 10-year retrospective cohort study in China.

A theoretical model that considers the diversity of collateral effects among drug resistance mutations provides insights into the development of robust sequential antibiotic treatments.