Discovering Biomarkers Associated And Predicting Cardiovascular Disease With High Accuracy Using A Novel Nexus Of Machine Learning Techniques For Precision Medicine

This article presents a comprehensive dataset of 61 CVD patients and 10 healthy individuals, which was used to develop a genotype-phenotype study. The dataset includes patient information such as age, gender, race, and gene expression data derived from RNA-seq. The authors propose the Clinically Integrated Genomics and Transcriptomics (CIGT) format to integrate heterogeneous clinical, demographic, genomic and transcriptomic patient data for predictive analysis.