{"id":2144,"date":"2026-05-28T04:51:39","date_gmt":"2026-05-28T04:51:39","guid":{"rendered":"https:\/\/sites.rutgers.edu\/precision-medicine\/?p=2144"},"modified":"2026-05-28T05:02:34","modified_gmt":"2026-05-28T05:02:34","slug":"translational-bioinformatics-and-machine-learning-framework-for-biomarker-discovery-disease-prediction-and-patient-profiling-for-precision-medicine","status":"publish","type":"post","link":"https:\/\/sites.rutgers.edu\/precision-medicine\/translational-bioinformatics-and-machine-learning-framework-for-biomarker-discovery-disease-prediction-and-patient-profiling-for-precision-medicine\/","title":{"rendered":"Translational bioinformatics and machine learning framework for biomarker discovery, disease prediction, and patient profiling for precision medicine"},"content":{"rendered":"

Our latest study, \u201cTranslational bioinformatics and machine learning framework for biomarker discovery, disease prediction, and patient profiling for precision medicine<\/strong><\/a>“.<\/p>\n

Precision medicine aims to advance our ability from a “one-size-fits-all” approach to personalized and predictive healthcare across diverse populations. It promotes integration of multi-omics and phenotypic data to understand disease mechanisms and discover novel biomarkers and risk factors, which could be used to predict and prevent critical diseases in individual patients across diverse populations. The potential implications of precision medicine approach can accelerate our ability to classify patients at higher risk of developing critical diseases, improve diagnostic capabilities, develop deeper understanding of individual risk, investigate racial differences and demographic characteristics, and find relationships between genetic variants, expressions, and diseases. This study focuses on implementing an innovative and data driven framework of translational bioinformatics and Machine Learning (ML) techniques to analyze multi-omics, including RNA-seq and Whole-Genome Sequencing (WGS) data, generated using blood samples of randomly consented patients. First, we utilized bioinformatics pipelines to identify differentially expressed genes and their pathogenic and likely pathogenic variants for the downstream data analysis, annotation, and visualization. Then, applied a nexus of ML models for multi-omics biomarker discovery, disease prediction, density-based clustering, single-patient profiling, and pathogenicity classification. WGS data analysis supported the exploration of genetic variation and diversity among patients to identify known and novel biomarkers, whereas RNA-seq data analysis improved our understanding of functional and biological pathways that underlying disease states. We classified and clustered pathogenic variants and expressions across various genes and discovered numerous diseases leading risk factors. Our results include gene-disease associations and captured common pathways across the broader population, demonstrating a level of sensitivity and accuracy that has broad clinical implications. We validated our results through clinical records, and state of the science literature. This study delves into the strengths of multi-omics data integration and capabilities of ML application in genetically diverse and complex patient cohorts. Our approach has the potential to elucidate complex gene-disease interactions for genetically diverse populations, which can support earlier diagnoses for patients in many disease realms.<\/p>\n

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