Identifying and characterizing disease subpopulations that most benefit from polygenic risk scores.

Polygenic risk scores (PRSs) hold promise in their potential translation into clinical settings to improve disease risk prediction. An important consideration in integrating PRSs into clinical settings is to gain an understanding of how to identify which subpopulations of individuals most benefit from PRSs for risk prediction. In this study, using the UK Biobank dataset, we trained logistic regression models to predict the 10 year incident risk of myocardial infarction, breast cancer, and schizophrenia using either just clinical features or clinical features combined with PRSs.

Early and fair COVID-19 outcome risk assessment using robust feature selection.

Personalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clinical outcomes using machine learning may provide a fast and data-driven solution for optimizing patient care by estimating the need for early treatment.

Integrating Multi-Omics Data With EHR for Precision Medicine Using Advanced Artificial Intelligence.

With the recent advancement of novel biomedical technologies such as high-throughput sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics molecular data to real-time continuous bio-signals are generated at an unprecedented speed and scale every day. For the first time, these multi-modal biomedical data are able to make precision medicine close to a reality. However, due to data volume and the complexity, making good use of these multi-modal biomedical data requires major effort.

Effect of case and control definitions on genome-wide association study (GWAS) findings.

Genome-wide association studies (GWAS) have significantly advanced our understanding of the genetic underpinnings of diseases, but case and control cohort definitions for a given disease can vary between different published studies. For example, two GWAS for the same disease using the UK Biobank data set might use different data sources (i.e.

Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era.

At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment.

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review.

Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during the pandemic, few have made a significant clinical impact, especially in diagnosis and disease precision staging. One major cause for such low impact is the lack of model transparency, significantly limiting the AI adoption in real clinical practice. To solve this problem, AI models need to be explained to users.

Highly elevated polygenic risk scores are better predictors of myocardial infarction risk early in life than later.

Background: Several polygenic risk scores (PRS) have been developed for cardiovascular risk prediction, but the additive value of including PRS together with conventional risk factors for risk prediction is questionable. This study assesses the clinical utility of including four PRS generated from 194, 46K, 1.5M, and 6M SNPs, along with conventional risk factors, to predict risk of ischemic heart disease (IHD), myocardial infarction (MI), and first MI event on or before age 50 (early MI).

Application of Combination High-Throughput Phenotypic Screening and Target Identification Methods for the Discovery of Natural Product-Based Combination Drugs.

Modern drug discovery efforts have had mediocre success rates with increasing developmental costs, and this has encouraged pharmaceutical scientists to seek innovative approaches. Recently with the rise of the fields of systems biology and metabolomics, network pharmacology (NP) has begun to emerge as a new paradigm in drug discovery, with a focus on multiple targets and drug combinations for treating disease. Studies on the benefits of drug combinations lay the groundwork for a renewed focus on natural products in drug discovery.

The impact of psychological distress during pregnancy on the developing fetus: biological mechanisms and the potential benefits of mindfulness interventions.

The in utero environment plays an essential role in shaping future growth and development. Psychological distress during pregnancy has been shown to perturb the delicate physiological milieu of pregnancy, and has been associated with negative repercussions in the offspring, including adverse birth outcomes, long-term defects in cognitive development, behavioral problems during childhood and high baseline levels of stress-related hormones. Fetal epigenetic programming, involving epigenetic processes, may help explain the link between maternal prenatal stress and its negative effects on the child.