Latest Research in Cancer Multi-Omics
A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Bioinformatics".
Deadline for manuscript submissions: 30 June 2025 | Viewed by 107
Special Issue Editor
Special Issue Information
Dear Colleagues,
We invite you to contribute to our Special Issue on “Latest Research in Cancer Multi-Omics”. The recent advancements in multi-omics approaches, driven partly by the integration of machine learning (ML)/artificial intelligence (AI), have become increasingly vital in translating molecular research into clinical applications, particularly in oncology. These technologies offer the comprehensive insights needed to address the heterogeneity and complexity of malignancies, which any single modality cannot sufficiently capture. This Special Issue focuses on the latest developments in multi-omics research, emphasizing translational studies. By integrating orthogonal data such as genomics, transcriptomics, proteomics, metabolomics, radiomics, pathomics, and others, we can gain deeper insights into cancer biology, leading to the identification of novel biomarkers, therapeutic targets, and the continued development of a more personalized approach to care.
This Special Issue aims to gather state-of-the-art research that demonstrates insights gained through multi-omics approaches in cancer. We encourage submissions that focus on integrating various omics datasets and their potential application. The scope also encompasses developing and applying machine learning and AI tools to enhance the analysis and interpretation of multi-omics data. Our objective is to provide a comprehensive collection of articles highlighting the latest scientific advancements and their potential to improve our understanding of cancer.
In this Special Issue, we welcome original research articles and reviews. Research areas may include (but are not limited to) the following:
Integration of Multi-Omics Data
- Approaches to combining genomics, transcriptomics, proteomics, and metabolomics in cancer research.
- Computational methods for multi-omics data integration.
- Challenges and solutions in multi-omics data analysis.
Single-Cell Multi-Omics
- Applications of single-cell multi-omics to understand cancer initiation, invasion, and metastasis.
- Technologies and methods in single-cell multi-omics.
Biomarker Discovery and Validation
- Identification of novel cancer biomarkers using multi-omics.
- Validation of multi-omics biomarkers for potential use.
Machine Learning in Multi-Omics Analysis
- Machine learning techniques for integrating and analyzing multi-omics data in cancer research.
- Development of predictive models for cancer outcomes using multi-omics and machine learning.
- Applications of artificial intelligence in identifying key biomarkers and therapeutic targets from multi-omics data.
We look forward to receiving your contributions.
Dr. Philip Anthony Sutera
Guest Editor
Manuscript Submission Information
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Keywords
- cancer
- multi-omics
- genomics
- transcriptomics
- proteomics
- metabolomics
- radiomics
- pathomics
- precision medicine
- artificial intelligence
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