Sudden cardiovascular diseases, including sudden cardiac arrest (SCA) and myocardial infarction (MI), remain among the leading causes of mortality worldwide. This study employs synthetic data simulations to develop polygenic risk scores (PRS) for improved identification of individuals at elevated risk. Leveraging genetic and phenotypic information from established literature, we modeled associations between multiple single nucleotide polymorphisms (SNPs) and the risk of sudden cardiovascular diseases. Our case study demonstrated that individuals with higher PRS values have a significantly increased risk of SCA and MI, with SNPs such as LTA (252A>G) identified as notable contributors. Additionally, combining PRS with traditional cardiovascular risk factors, such as smoking and diabetes, improved predictive accuracy, underscoring the value of integrating genetic data with clinical variables. These findings highlight the cumulative effect of genetic predispositions in determining cardiovascular risk and suggest potential applications of PRS models in personalized medicine to enhance preventive strategiesWhile promising, the study recognizes limitations related to the use of synthetic data and the need for validation in diverse, real-world populations. Future research should focus on refining these models and exploring additional genetic markers to further improve prediction capabilities.

Polygenetic Risk Scores are used to evaluate an individual's vulnerability to developing specific diseases or conditions based on their genetic composition, by taking into account numerous genetic variations. This article provides an overview of the concept of Polygenic Risk Scores (PRS). We elucidate the historical advancements of PRS, their advantages and shortcomings in comparison with other predictive methods, and discuss their conceptual limitations in light of the complexity of biological systems. Furthermore, we provide a survey of published tools for computing PRS and associated resources. The various tools and software packages are categorized based on their technical utility for users or prospective developers. Understanding the array of available tools and their limitations is crucial for accurately assessing and predicting disease risks, facilitating early interventions, and guiding personalized healthcare decisions. Additionally, we also identify potential new avenues for future bioinformatic analyzes and advancements related to PRS.

The polygenic risk score has proven to be a valuable tool for assessing an individual's genetic predisposition to phenotype (disease) within biomedicine in recent years. However, traditional regression-based methods for polygenic risk scores calculation have limitations that can impede their accuracy and predictive power. This study introduces an innovative approach to enhance polygenic risk scores calculation through the application of genetic programming. By harnessing the power of genetic programming, we aim to overcome the limitations of traditional regression techniques and improve the accuracy of polygenic risk scores predictions. Specifically, we showed that a polygenic risk score generated through Cartesian genetic programming yielded comparable or even more robust statistical distinctions between groups that we evaluated within three independent case studies.

Antibiotic use in farming for decades has led to the selection pressure on chicken commensal bacteria which adapted to those changes by the acquisition of antibiotic resistance genes via horizontal gene transfer. Extensive gene transfer between gut bacteria is mediated by mobile genetic elements such as plasmids, integrative conjugative elements and temperate phages. Therefore, chicken gut commensal bacteria are considered reservoirs of antibiotic resistance genes. Recently, we initiated a systemic cultivation of chicken gut anaerobes followed by subsequent whole genome sequencing and analysis. In this study, we implemented a novel in silico approach using systems biology methods to detect horizontal gene traits and mobile genetic elements associated with it in the genomes of chicken gut commensals. In total, we identified 1,427 genes envisaged to drive horizontal gene transfer between individual chicken gut microbiota members across different families. Except for genes known to be vertically transferred, we also revealed hypothetical genes which may represent important, yet unknown section of mobilome.

One of the most challenging scenarios of data analysis is prediction using time series data. As the underlying causal relationships of the data shift over time, a classification model trained on data at earlier points within the course starts to yield incorrect predictions on the current data. This phenomenon in machine learning is called concept drift. Within biomedical data, one of the molecular networks that changes significantly over a time is the metabolome. Using metabolomics analysis in biomedical applications produces an ideal tool in preventive healthcare, the pharmaceutical industry, and even ecology engineering. This study provides an innovative perspective on the analysis of metabolomics datasets using the concept of drift detection. The evaluation is based on two main objectives. The first objective is connected to the concept drift detection in available metabolomics datasets, and the second objective is to provide the assessment of commonly used machine learning tools for the best general detection approach in metabolomics datasets. The application of concept drift to metabolomics data has never been carried out before and is an original take on the analysis of highly dynamic molecular networks.