About This Special Issue
Background and significance: Current high-throughput technology such as genomics, transcriptomics, proteomics, metabolomics, and other "omics" approaches have made extensive molecular data available for investigating cancer. The challenges lie in integrating data from different omics fields that can provide a comprehensive and holistic understanding of complex molecular mechanisms in cancer development and progression.
Objective: To understand the molecular basis of cancer heterogeneity, identify subtypes and biomarkers, and develop effective therapeutic through data-driven integration approaches.
Broad theme and specific area of focus: Omics integrative approaches help develop personalized treatments based on specific molecular characteristics of the patients.
Expected Outcome: Through this special issue, we aim to utilize multi-level molecular information to understand a cancer diagnosis, prognosis, and classification to develop more effective treatment therapies. More specifically, we want to emphasize the development of novel methods that can capture valuable information from large-scale cancer molecular data and help understand tumor complexity.
We welcome researchers from various disciplines to provide interdisciplinary perspectives on ‘Omics Integration for Cancer Biology Insights.’ Your contributions will play a crucial role in advancing knowledge in this field.
Potential topics include, but are not limited to:
- Predictive modeling of cancer progression using multi-omics data
- Identification of multi-omics signatures for early cancer detection
- Personalized treatment response prediction in cancer patients
- Exploring the role of non-coding RNAs in cancer using multi-omics approaches
- Network-based integration of multi-omics data for cancer subtyping
- Machine/Deep learning for image and multi-omics fusion in cancer diagnosis
- Temporal analysis of multi-omics data for understanding cancer evolution
- Cancer evolutionary trajectories through multi-omics profiling
- Predictive modeling of drug response in cancer using multi-omics data
- Machine/Deep learning approaches for integrating spatial and multi-omics data in tumor microenvironment
