{"id":1694,"date":"2024-11-10T00:00:48","date_gmt":"2024-11-10T00:00:48","guid":{"rendered":"https:\/\/sites.rutgers.edu\/precision-medicine\/?p=1694"},"modified":"2025-05-15T05:47:44","modified_gmt":"2025-05-15T05:47:44","slug":"we-are-presenting-intelligenes-amia-2024-annual-symposium","status":"publish","type":"post","link":"https:\/\/sites.rutgers.edu\/precision-medicine\/we-are-presenting-intelligenes-amia-2024-annual-symposium\/","title":{"rendered":"IntelliGenes @ AMIA Annual Symposium 2024"},"content":{"rendered":"

Dr. Ahmed presents IntelliGenes<\/strong><\/em> <\/a>at the American Medical Informatics Association (AMIA) Annual Symposium<\/a>, November 9 – 13, 2024 in San Francisco, CA.<\/p>\n

IntelliGenes: A novel, interactive, customizable, and user-friendly AI\/ML application for biomarker discovery and predictive analysis<\/strong> <\/a><\/p>\n

Presentation Time:<\/strong> 03:45 PM – 04:00 PM
\nPrimary Track:<\/strong> Applications
\nProgrammatic Theme:<\/strong> Translational Bioinformatics<\/p>\n

Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life, however, its progress in the field of genomics is not matching the levels others have attained. Challenges include but are not limited to the handling and analysis of high volumes of complex genomic data, and the expertise needed to implement and execute AI\/ML approaches. In this study, we present IntelliGenes, a novel, interactive, customizable, cross-platform, and user-friendly AI\/ML application for multi-genomic data exploration to discover novel biomarkers and predict rare, common, and complex diseases. The implemented methodology is based on a nexus of conventional statistical techniques and cutting-edge ML algorithms. The interactive and cross-platform graphical user interface of IntelliGenes is divided into three main sections: 1) Data Manager, 2) AI\/ML Analysis, and 3) Visualization. Data Manager supports the user in loading and customizing the input data and list of existing biomarkers. AI\/ML Analysis allows the user to apply default combinations of statistical and ML algorithms, as well as customize and create new AI\/ML pipelines. Visualization provides options to interpret produced results. The performance of IntelliGenes has been successfully tested at variable in-house and peer reviewed studies to discover biomarkers associated with and to predict cardiovascular diseases. We have designed and implemented it in a way that the user with and without computational background can apply AI\/ML approaches to discover novel biomarkers and predict diseases.<\/p>\n

Authors:<\/strong>
\nWilliam DeGroat<\/strong>; Rishabh Narayanan,<\/strong> Dinesh Mendhe, <\/strong>Habiba Abdelhalim<\/strong>; and Zeeshan Ahmed, PhD<\/strong> – Department of Medicine, Rutgers Robert Wood Johnson Medical School. Rutgers Institute for Health, Health Care Policy and Aging Research. Rutgers Biomedical and Health Sciences. Rutgers The State University of New Jersey.<\/em><\/p>\n

IntelliGenes,<\/em> Project Publications:<\/strong><\/p>\n