Chapter title |
Systems Analysis of High-Throughput Data.
|
---|---|
Chapter number | 8 |
Book title |
A Systems Biology Approach to Blood
|
Published in |
Advances in experimental medicine and biology, January 2014
|
DOI | 10.1007/978-1-4939-2095-2_8 |
Pubmed ID | |
Book ISBNs |
978-1-4939-2094-5, 978-1-4939-2095-2
|
Authors |
Rosemary Braun, Braun R |
Abstract |
Modern high-throughput assays yield detailed characterizations of the genomic, transcriptomic, and proteomic states of biological samples, enabling us to probe the molecular mechanisms that regulate hematopoiesis or give rise to hematological disorders. At the same time, the high dimensionality of the data and the complex nature of biological interaction networks present significant analytical challenges in identifying causal variations and modeling the underlying systems biology. In addition to identifying significantly disregulated genes and proteins, integrative analysis approaches that allow the investigation of these single genes within a functional context are required. This chapter presents a survey of current computational approaches for the statistical analysis of high-dimensional data and the development of systems-level models of cellular signaling and regulation. Specifically, we focus on multi-gene analysis methods and the integration of expression data with domain knowledge (such as biological pathways) and other gene-wise information (e.g., sequence or methylation data) to identify novel functional modules in the complex cellular interaction network. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 2% |
Czechia | 1 | 2% |
Unknown | 55 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 15 | 26% |
Student > Ph. D. Student | 13 | 23% |
Student > Master | 8 | 14% |
Student > Bachelor | 2 | 4% |
Student > Doctoral Student | 2 | 4% |
Other | 7 | 12% |
Unknown | 10 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 14 | 25% |
Biochemistry, Genetics and Molecular Biology | 12 | 21% |
Computer Science | 4 | 7% |
Medicine and Dentistry | 4 | 7% |
Physics and Astronomy | 3 | 5% |
Other | 9 | 16% |
Unknown | 11 | 19% |