Title |
A roadmap towards personalized immunology
|
---|---|
Published in |
npj Systems Biology and Applications, February 2018
|
DOI | 10.1038/s41540-017-0045-9 |
Pubmed ID | |
Authors |
Sylvie Delhalle, Sebastian F. N. Bode, Rudi Balling, Markus Ollert, Feng Q. He |
Abstract |
Big data generation and computational processing will enable medicine to evolve from a "one-size-fits-all" approach to precise patient stratification and treatment. Significant achievements using "Omics" data have been made especially in personalized oncology. However, immune cells relative to tumor cells show a much higher degree of complexity in heterogeneity, dynamics, memory-capability, plasticity and "social" interactions. There is still a long way ahead on translating our capability to identify potentially targetable personalized biomarkers into effective personalized therapy in immune-centralized diseases. Here, we discuss the recent advances and successful applications in "Omics" data utilization and network analysis on patients' samples of clinical trials and studies, as well as the major challenges and strategies towards personalized stratification and treatment for infectious or non-communicable inflammatory diseases such as autoimmune diseases or allergies. We provide a roadmap and highlight experimental, clinical, computational analysis, data management, ethical and regulatory issues to accelerate the implementation of personalized immunology. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 9 | 23% |
United Kingdom | 3 | 8% |
Argentina | 2 | 5% |
Romania | 2 | 5% |
Singapore | 2 | 5% |
Norway | 1 | 3% |
Luxembourg | 1 | 3% |
Canada | 1 | 3% |
Russia | 1 | 3% |
Other | 3 | 8% |
Unknown | 15 | 38% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 25 | 63% |
Scientists | 12 | 30% |
Practitioners (doctors, other healthcare professionals) | 3 | 8% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 187 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 43 | 23% |
Student > Ph. D. Student | 21 | 11% |
Student > Bachelor | 18 | 10% |
Student > Doctoral Student | 14 | 7% |
Other | 13 | 7% |
Other | 43 | 23% |
Unknown | 35 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 39 | 21% |
Agricultural and Biological Sciences | 20 | 11% |
Medicine and Dentistry | 20 | 11% |
Immunology and Microbiology | 18 | 10% |
Unspecified | 10 | 5% |
Other | 32 | 17% |
Unknown | 48 | 26% |