Deep metabolic phenotyping of humans with protein-altering variants in using a genome-first approach.

Background & Aim: An unbiased genome-first approach can expand the molecular understanding of specific genes in disease-agnostic biobanks for deeper phenotyping. represents a good candidate for this approach due to its known association with steatotic liver disease (SLD).

Methods: We screened participants with whole-exome sequences in the Penn Medicine Biobank (PMBB, n >40,000) and the UK Biobank (UKB, n >200,000) for protein-altering variants in and evaluated their association with liver phenotypes and clinical outcomes.

Optimization of Smoking Classification by Applying Neural Network with Variable Importance Using Cytokine Biomarkers.

Cigarette smoking is a preventable epidemic that is a leading cause of death. It increases the risk of coronary heart disease, stroke, lung cancer, chronic obstructive lung diseases etc., multifold.

Logistic Regression and Statistical Regularization Techniques for Risk Classification of Coronary Artery Disease using Cytokines transported by high density lipoproteins.

Coronary artery disease (CAD) is a leading cause of mortality in the world. It is important to be able to proactively assess the risk of the disease, using novel biomarkers like cytokines that are indicators of inflammation in addition to traditional predictors of risk. Atherosclerosis, the primary cause of CAD, is an inflammatory disease involving cytokines.

Smoking Classification Using Novel Plasma Cytokines by implementing Machine Learning and Statistical Methods.

Smoking is a major cause of premature and preventable death. Tobacco exposure has a detrimental effect on many organs and contributes to multiple diseases including chronic obstructive pulmonary disease (COPD), cardiovascular disease, cancer, and diabetes. Cytokines are inflammatory biomarkers that are mechanistically associated with smoking.