Journal
BMC Cardiovascular Disorders
Exploring all aspects of research related to disorders of the heart and circulatory system, BMC Cardiovascular Disorders is a well-established open access peer-reviewed journal with a...
BMC Cardiovascular Disorders is calling for submissions to our Collection on Omics approaches to understand cardiovascular disease. Omics approaches have emerged as indispensable tools in unraveling the intricate molecular landscape of cardiovascular disease (CVD) by providing comprehensive insights into the underlying mechanisms driving CVD pathogenesis, progression, and response to therapy. Integrative omics approaches further enhance our understanding by integrating multi-omics data to explain complex molecular networks and identify novel disease pathways.
The application of omics technologies holds immense promise in advancing precision medicine approaches for CVD management. By leveraging omics data, clinicians can stratify patients based on their molecular profiles, enabling tailored treatment strategies and improving therapeutic outcomes. Omics-based biomarkers offer non-invasive tools for early disease detection, risk prediction, and monitoring disease progression. Omics approaches represent a transformative paradigm in cardiovascular research and clinical practice, offering unprecedented opportunities to decipher the molecular intricacies of CVD and revolutionize patient care.
In support of SDG 3: Good Health & Wellbeing, this Collection seeks contributions that address a spectrum of topics, including but not limited to:
-Genomics
-Transcriptomics
-Proteomics
-Metabolomics
-Epigenomics
-Integrative Omics Analyses
-Omics-based Biomarker Discovery
-Molecular Pathways in Cardiovascular Disease
-Personalized Medicine Approaches
-Therapeutic Targets and Drug Development
-Clinical Applications of Omics Technologies in Cardiovascular Medicine
Submit your manuscript to this collection through the participating journal.
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Zeeshan Ahmed
Dr Zeeshan Ahmed is an Assistant Professor at the Department of Medicine, Rutgers Robert Wood Johnson Medical School, and Core Faculty Member at the Institute for Health, Health Care Policy and Aging Research, Rutgers Health. His lab at Rutgers is focused on implementing novel Artificial Intelligence and Machine Learning (AI/ML), and orthodox bioinformatics and biomedical informatics approaches to investigate multi-modal multi-omics and phenotypic data for the identification of patterns revealing predictive biomarkers and risk factors to support earlier diagnosis of patients with complex, common, and rare diseases. -
Goo Jun
Dr Goo Jun is an Associate Professor of Epidemiology at the UTHealth Houston School of Public Health. After receiving a PhD in Electrical and Computer Engineering from The University of Texas at Austin, he worked as a postdoctoral researcher at the Center for Statistical Genetics at the University of Michigan. He works on statistical genetics, computational biology, bioinformatics, genomics and multi-omics data analysis. His research is focused on development of computational and statistical methods for analysis of massive data to understand genetics and biology of complex traits.