Multi-omics analysis of genetic drivers linking aortic stenosis and left ventricular diastolic dysfunction in heart failure
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- Title: Subtitle
- Multi-omics analysis of genetic drivers linking aortic stenosis and left ventricular diastolic dysfunction in heart failure
- Creators
- Zeeshan Ahmed - Rutgers, The State University of New JerseyPrithvi Govindareddy - Rutgers, The State University of New JerseyJayden Mathew - Rutgers, The State University of New JerseyNaveena V. Yanamala - Johnson UniversityPartho Sengupta - Johnson University
- Publication Details
- BioData mining, Vol.19(1)
- Date published
- 06/11/2026
- Publisher
- BMC; LONDON
- Number of pages
- 32
- Grant note
- Rutgers University, through Rutgers Health Center for Biomedical Informatics & Health Artificial Intelligence (BMIHAI) Pilot Grant Program
This research was conducted with the great support of Division of Cardiovascular Diseases and Hypertension, Department of Medicine, Robert Wood Johnson Medical School (RWJMS), and Rutgers Institute for Health, Health Care Policy, and Aging Research (IFH), Rutgers Health, NJ. We express sincere gratitude to the current and former members of Ahmed Lab at Rutgers RWJMS/IFH, and appreciate all colleagues, collaborators and institutions who provided direct and indirect insight and expertise that greatly assisted the research and development of this project. We acknowledge the Office of Advanced Research Computing (OARC) at Rutgers University for providing access to the Amarel cluster and associated research computing resources that have contributed to the development and testing of this application. Reported research in this publication was supported by Rutgers University, through Rutgers Health Center for Biomedical Informatics & Health Artificial Intelligence (BMIHAI) Pilot Grant Program to Z.A. The contentis solely the responsibility of the authors and does not necessarily represent the official views of the institutions.
- Academic Unit
- Institute for Health, Health Care Policy and Aging Research
- Language
- English
- Resource Type
- Journal article
- Identifiers
- 991032316587604646