Predicting early-stage coronary artery disease using machine learning and routine clinical biomarkers improved by augmented virtual data.

Aims: Coronary artery disease (CAD) is a highly prevalent disease with modifiable risk factors. In patients with suspected obstructive CAD, evaluating the pre-test probability model is crucial for diagnosis, although its accuracy remains controversial. Machine learning (ML) predictive models can help clinicians detect CAD early and improve outcomes.

3D clustering of gene expression data from systemic autoinflammatory diseases using self-organizing maps (Clust3D).

Background And Objective: Systemic autoinflammatory diseases (SAIDs) are characterized by widespread inflammation, but for most of them there is a lack of specific biomarkers for accurate diagnosis. Although a number of machine learning algorithms have been used to analyze SAID datasets, aiding in the discovery of novel biomarkers, there is a growing recognition of the importance of SAID timeseries clustering, as it can capture the temporal dynamics of gene expression patterns.

Methodology: This paper proposes a novel clustering methodology to efficiently associate three-dimensional data.

Genes encoding γ‑glutamyl‑transpeptidases in the allicin biosynthetic pathway in garlic ().

Allicin is a thiosulphate molecule produced in garlic () and has a wide range of biological actions and pharmaceutical applications. Its precursor molecule is the non-proteinogenic amino acid alliin (S-allylcysteine sulphoxide). The alliin biosynthetic pathway in garlic involves a group of enzymes, members of which are the γ-glutamyl-transpeptidase isoenzymes, γ-glutamyl-transpeptidase AsGGT1, AsGGT2 and AsGGT3, which catalyze the removal of the γ-glutamyl group from γ-glutamyl-S-allyl-L-cysteine to produce S-allyl-L-cysteine.

Classification of Inflammation of Unknown Origin patients based on RNA-seq and SomaScan data.

A preliminary analysis was conducted on data acquired from RNA sequencing and SomaScan platforms, for the classification of patients with Inflammation of Unknown Origin. To this end, a multimodal data integration approach was designed, by combining the two platforms, in order to assess the potentiality of learning estimators, using the differentially expressed features from the independent profiling experiments of both platforms. The classification framing was the differentiation of Inflammation of Unknown Origin patients against a multitude of Systemic Autoinflammatory disease patients.

A comparison study on creating simulated patient data for individuals suffering from chronic coronary disorders.

An emerging area in data science that has lately gained attention is the virtual population (VP) and synthetic data generation. This field has the potential to significantly affect the healthcare industry by providing a means to augment clinical research databases that have a shortage of subjects. The current study provides a comparative analysis of five distinct approaches for creating virtual data populations from real patient data.