| Title |
Systems Medicine
|
|---|---|
| Published by |
Methods in molecular biology, January 2016
|
| DOI | 10.1007/978-1-4939-3283-2 |
| Pubmed ID | |
| ISBNs |
978-1-4939-3282-5, 978-1-4939-3283-2
|
| Authors |
Caie, Peter D, Harrison, David J, Bowness, Ruth, Schmitz, Ulf, Wolkenhauer, Olaf |
| Editors |
Ulf Schmitz, Olaf Wolkenhauer |
| Abstract |
The field of pathology is rapidly transforming from a semiquantitative and empirical science toward a big data discipline. Large data sets from across multiple omics fields may now be extracted from a patient's tissue sample. Tissue is, however, complex, heterogeneous, and prone to artifact. A reductionist view of tissue and disease progression, which does not take this complexity into account, may lead to single biomarkers failing in clinical trials. The integration of standardized multi-omics big data and the retention of valuable information on spatial heterogeneity are imperative to model complex disease mechanisms. Mathematical modeling through systems pathology approaches is the ideal medium to distill the significant information from these large, multi-parametric, and hierarchical data sets. Systems pathology may also predict the dynamical response of disease progression or response to therapy regimens from a static tissue sample. Next-generation pathology will incorporate big data with systems medicine in order to personalize clinical practice for both prognostic and predictive patient care. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 2 | 11% |
| United States | 2 | 11% |
| United Kingdom | 1 | 6% |
| Italy | 1 | 6% |
| Finland | 1 | 6% |
| Netherlands | 1 | 6% |
| Unknown | 10 | 56% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 14 | 78% |
| Scientists | 3 | 17% |
| Practitioners (doctors, other healthcare professionals) | 1 | 6% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 1% |
| Unknown | 77 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 5% |
| Student > Bachelor | 3 | 4% |
| Researcher | 3 | 4% |
| Other | 2 | 3% |
| Student > Master | 2 | 3% |
| Other | 3 | 4% |
| Unknown | 61 | 78% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 4 | 5% |
| Agricultural and Biological Sciences | 4 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 3% |
| Medicine and Dentistry | 2 | 3% |
| Computer Science | 2 | 3% |
| Other | 3 | 4% |
| Unknown | 61 | 78% |