The four key methods to stratify chronic kidney disease risk are genomics, multiomics,
supervised and unsupervised machine learning methods. Polygenic risk scores utilize …

With biomedical research transitioning into data-rich science, machine learning provides a
powerful toolkit for extracting knowledge from large-scale biological data sets. The …

Chronic kidney diseases (CKDs) are currently classified according to their clinical features,
associated comorbidities and pattern of injury on biopsy. Even within a given classification …

Background Although chronic kidney disease (CKD) is associated with high multimorbidity,
polypharmacy, morbidity and mortality, existing classification systems (mild to severe …

In the past decade, there have been tremendous strides in both the large-scale generation
of phenotypic data with the advent of electronic health record (EHR) systems and the …

In recent years, biology and precision medicine have benefited from major advancements in
generating large-scale molecular and biomedical datasets and in analyzing those data …

Current classification of chronic kidney disease (CKD) into stages using indirect systemic
measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the …

Abstract Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is
typically silent until late stages, but early intervention can significantly delay its progression …

Chronic kidney disease (CKD) is a significant global health challenge that requires timely
detection and accurate prognosis for effective treatment and management. The application …

A new study reports that genome-wide polygenic risk scores can identify individuals at risk of
common complex diseases, such as coronary artery disease or type 2 diabetes, with …