Genomics, proteomics and metabolomics ("omics") data being generated on clinical tumors has the potential to transform cancer care through high-throughput analysis of patient-derived tumors and promote “precision” medicine through tumor molecular profiling. High-throughput technologies of molecular profiling at various levels are evolving very rapidly, but computational approaches for interpreting big-data generated by those technologies into clinical progress are lagging. The imminent problems include data storage and management, data security and accessibility, data visualization, and data mining. Cancer bioinformatics is key for the success of the cancer translational and clinical research. The mission is to develop genetic risk prediction models, novel biomarkers, reveal mechanism of cancer cell development and explore treatment strategies for cancer. The ultimate goal is to help doctors fully deliver on the promise of precision oncology and to create better treatment options for all people with cancer.
Precision cancer medicine focuses on patient’s disease at the genetic level and seeks to find targeted treatments for each individual’s cancer. Realization of the vision of precision medicine will require collaboration among researchers with different disciplines including biomedical scientists, clinicians, molecular evolutionist and bioinformaticians.
This Research Topic focuses on cancer informatics and is intended to present and discuss innovative reports, methodologies, tools and algorithms that enable precision cancer medicine related to:
• Cancer genomic variation (SNP, CNV) and phenotypic correlation
• GWAS study
• Gene ontology, Pathway, interactome and Network analysis
• Cancer genomic/transcriptomic/proteomic analysis
• Comparative genomics and molecular evolution
• Target sequencing and cancer diagnostic panel/protocol development
• Data mining, visualization, machine learning and statistically modelling
• Software, web-tools, and databases development
• High-performance computing system application
• Electronic health record/informatics systems
• Single-Cell Analysis in Cancer Genomics
• Identification and prediction of neoantigens of cancer
• Neoantigens and cancer vaccine and CAR-T
Genomics, proteomics and metabolomics ("omics") data being generated on clinical tumors has the potential to transform cancer care through high-throughput analysis of patient-derived tumors and promote “precision” medicine through tumor molecular profiling. High-throughput technologies of molecular profiling at various levels are evolving very rapidly, but computational approaches for interpreting big-data generated by those technologies into clinical progress are lagging. The imminent problems include data storage and management, data security and accessibility, data visualization, and data mining. Cancer bioinformatics is key for the success of the cancer translational and clinical research. The mission is to develop genetic risk prediction models, novel biomarkers, reveal mechanism of cancer cell development and explore treatment strategies for cancer. The ultimate goal is to help doctors fully deliver on the promise of precision oncology and to create better treatment options for all people with cancer.
Precision cancer medicine focuses on patient’s disease at the genetic level and seeks to find targeted treatments for each individual’s cancer. Realization of the vision of precision medicine will require collaboration among researchers with different disciplines including biomedical scientists, clinicians, molecular evolutionist and bioinformaticians.
This Research Topic focuses on cancer informatics and is intended to present and discuss innovative reports, methodologies, tools and algorithms that enable precision cancer medicine related to:
• Cancer genomic variation (SNP, CNV) and phenotypic correlation
• GWAS study
• Gene ontology, Pathway, interactome and Network analysis
• Cancer genomic/transcriptomic/proteomic analysis
• Comparative genomics and molecular evolution
• Target sequencing and cancer diagnostic panel/protocol development
• Data mining, visualization, machine learning and statistically modelling
• Software, web-tools, and databases development
• High-performance computing system application
• Electronic health record/informatics systems
• Single-Cell Analysis in Cancer Genomics
• Identification and prediction of neoantigens of cancer
• Neoantigens and cancer vaccine and CAR-T
