The rapid development of biotechnology provides us with a large number of biological systems from different levels of omics data, including genomics, transcriptomics, proteomics, metabolomics, and epigenetics. Oncology research based on single-omics data is increasingly mature, while the integration of multi-omics data research is just starting. Tumor is a complex regulatory system, and the limitations of using a single omics data study are becoming more and more significant. The integration of multi-omics approaches is expected to elucidate the mechanism of tumor occurrence and development, discover biomarkers with diagnostic and prognostic functions, explore new therapeutic targets, and ultimately realize tumor prediction, prevention, and individualized or personalized medicine.
The goal of this research topic is to analyze the differences in clinical-pathological information, prognosis, immune microenvironment, metabolic characteristics, et al. among different subtypes of tumors by molecular classification. Through the risk stratification, patients can be diagnosed and prognosis predicted more accurately, and individualized diagnosis and treatment plans are provided for patients. The focus will be on papers linking bioinformatics and the wet lab; However, papers will be considered case by case based on overall merit.
This Research Topic aims to highlight the solid tumor risk stratification model constructed using multi-omics data and the predictive and diagnostic ability of the model was evaluated. We are particularly interested in, but not limited to, research in the field of multi-omics, machine learning, and cancer classification. We also welcome submissions of multi-omics analysis in complex diseases to identify new diagnostic biomarkers or prognostic markers.
We invite contributions that include, but are not limited to:
• Integration of cancer diagnosis models using multi-omics data
• New prognostic classification of cancer-based on multi-omics data analysis
• Machine/deep learning tools for analysis
• Single-cell data analysis
• Multi-omics integrative analysis method
• Copy number variation analysis of cancer
• Methylation analysis of cancer
• The analysis of gene–gene interaction networks
• Drug target discovery and design
• Network pharmacology
The rapid development of biotechnology provides us with a large number of biological systems from different levels of omics data, including genomics, transcriptomics, proteomics, metabolomics, and epigenetics. Oncology research based on single-omics data is increasingly mature, while the integration of multi-omics data research is just starting. Tumor is a complex regulatory system, and the limitations of using a single omics data study are becoming more and more significant. The integration of multi-omics approaches is expected to elucidate the mechanism of tumor occurrence and development, discover biomarkers with diagnostic and prognostic functions, explore new therapeutic targets, and ultimately realize tumor prediction, prevention, and individualized or personalized medicine.
The goal of this research topic is to analyze the differences in clinical-pathological information, prognosis, immune microenvironment, metabolic characteristics, et al. among different subtypes of tumors by molecular classification. Through the risk stratification, patients can be diagnosed and prognosis predicted more accurately, and individualized diagnosis and treatment plans are provided for patients. The focus will be on papers linking bioinformatics and the wet lab; However, papers will be considered case by case based on overall merit.
This Research Topic aims to highlight the solid tumor risk stratification model constructed using multi-omics data and the predictive and diagnostic ability of the model was evaluated. We are particularly interested in, but not limited to, research in the field of multi-omics, machine learning, and cancer classification. We also welcome submissions of multi-omics analysis in complex diseases to identify new diagnostic biomarkers or prognostic markers.
We invite contributions that include, but are not limited to:
• Integration of cancer diagnosis models using multi-omics data
• New prognostic classification of cancer-based on multi-omics data analysis
• Machine/deep learning tools for analysis
• Single-cell data analysis
• Multi-omics integrative analysis method
• Copy number variation analysis of cancer
• Methylation analysis of cancer
• The analysis of gene–gene interaction networks
• Drug target discovery and design
• Network pharmacology
