{"id":1930,"date":"2025-08-15T00:00:41","date_gmt":"2025-08-15T00:00:41","guid":{"rendered":"https:\/\/sites.rutgers.edu\/precision-medicine\/?p=1930"},"modified":"2025-10-02T19:24:36","modified_gmt":"2025-10-02T19:24:36","slug":"3d-intelligenes-ai-ml-application-using-multi-omics-data-for-biomarker-discovery-and-disease-prediction-with-multi-dimensional-visualization","status":"publish","type":"post","link":"https:\/\/sites.rutgers.edu\/precision-medicine\/3d-intelligenes-ai-ml-application-using-multi-omics-data-for-biomarker-discovery-and-disease-prediction-with-multi-dimensional-visualization\/","title":{"rendered":"3D IntelliGenes: AI\/ML application with multi-dimensional visualization for predictive analysis"},"content":{"rendered":"

Our latest article, “3D IntelliGenes<\/em>: AI\/ML application using multi-omics data for biomarker discovery and disease prediction with multi-dimensional visualization<\/strong><\/a>“, published in BMC Medical Research Methodology, collection: High-dimensional statistics and omics data analysis.<\/p>\n

Abstract:<\/strong><\/p>\n

The cutting-edge artificial intelligence (AI) and machine learning (ML) techniques have proven effective at uncovering elucidative knowledge on disease-causing biomarkers and the biological underpinnings of a plethora of human diseases. However, the high-dimensional nature of multi-omics data presents numerous challenges in its effective presentation, annotation, and interpretation. Traditional 2D visualizations often fall short in capturing the intricate relationships between multi-omics features, hindering our ability to identify meaningful correlations. In this study, we focused on addressing such challenges by developing an innovative solution to better visualize results produced by AI\/ML approaches on integrated clinical and multi-omics data for novel biomarker discovery and predictive analysis. We present an advanced version of our earlier published software with intuitive and interactive visualizations of multi-omics data in multi-dimensions i.e., 3D IntelliGenes, which offers deeper insights, most importantly by capturing greater variability in the patient data by understanding both linear and non-linear structures, evaluating AI\/ML model performance, and delineating the joint impact of biomarkers on the corresponding disease states. The overall functionality of 3D IntelliGenes is divided into two modules, data clustering and feature plotting. The data clustering module creates configurable 3D scatter plots to visualize the structure-preserving distribution of disease states, AI\/ML classifier bias in the form of type I\/II errors, and patient similarity through a robust density-driven clustering algorithm. Whereas the feature plotting module supports the joint analysis of pairs of multi-omics features to analyze the interdependence and discriminative power of co-expressed biomarkers. We report evaluated performance of 3D IntelliGenes using diverse cohorts of patients with cardiovascular and other diseases.<\/p>\n

Publication\/Citation:<\/strong><\/p>\n