Multi-omics Approaches for Biomarker Discovery in Early Ovarian Cancer Diagnosis
A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Disease Biomarker".
Deadline for manuscript submissions: 25 October 2024 | Viewed by 87
Special Issue Editor
Special Issue Information
Dear Colleagues,
Ovarian cancer presents a significant challenge in oncology, often diagnosed at advanced stages with limited treatment options. The emergence of multi-omics approaches offers promising avenues for early disease detection and personalized treatment strategies. This Special Issue explores the integration of diverse molecular data sets to identify robust biomarkers for early ovarian cancer diagnosis. Researchers can uncover molecular signatures indicative of ovarian cancer onset and progression by leveraging genomics, transcriptomics, proteomics, metabolomics, and other omics platforms. These comprehensive analyses can potentially enhance diagnostic accuracy, prognostic assessment, and therapeutic decision-making in ovarian cancer management. Through collaborative efforts across disciplines, including bioinformatics, computational biology, and clinical oncology, this Special Issue seeks to advance our understanding of the molecular landscape of ovarian cancer and identify clinical applications. By exploring the complex interplay between the biological pathways and molecular alterations of ovarian cancer, we will enable more effective personalized medicine interventions and improved patient outcomes. Furthermore, bioinformatics approaches, including traditional pipelines and new artificial intelligence models, may be crucial for analyzing multi-omics data required for biomarker discovery and clinical decision-making support.
Dr. Abedalrhman Alkhateeb
Guest Editor
Manuscript Submission Information
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Keywords
- ovarian cancer
- multi-omics data integration
- biomarker discovery
- early diagnosis
- personalized medicine
- genomics
- transcriptomics
- proteomics
- metabolomics
- clinical oncology
- bioinformatics
