Title |
Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets
|
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Published in |
Genome Medicine, May 2012
|
DOI | 10.1186/gm340 |
Pubmed ID | |
Authors |
Piyush B Madhamshettiwar, Stefan R Maetschke, Melissa J Davis, Antonio Reverter, Mark A Ragan |
Abstract |
Altered networks of gene regulation underlie many complex conditions, including cancer. Inferring gene regulatory networks from high-throughput microarray expression data is a fundamental but challenging task in computational systems biology and its translation to genomic medicine. Although diverse computational and statistical approaches have been brought to bear on the gene regulatory network inference problem, their relative strengths and disadvantages remain poorly understood, largely because comparative analyses usually consider only small subsets of methods, use only synthetic data, and/or fail to adopt a common measure of inference quality. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 40% |
United States | 1 | 20% |
Bosnia and Herzegovina | 1 | 20% |
Brazil | 1 | 20% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 60% |
Scientists | 2 | 40% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 4 | 2% |
United States | 4 | 2% |
Brazil | 3 | 1% |
United Kingdom | 1 | <1% |
Canada | 1 | <1% |
Ukraine | 1 | <1% |
China | 1 | <1% |
Denmark | 1 | <1% |
Unknown | 234 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 66 | 26% |
Researcher | 55 | 22% |
Student > Master | 30 | 12% |
Student > Bachelor | 17 | 7% |
Professor > Associate Professor | 14 | 6% |
Other | 39 | 16% |
Unknown | 29 | 12% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 90 | 36% |
Computer Science | 51 | 20% |
Biochemistry, Genetics and Molecular Biology | 31 | 12% |
Engineering | 13 | 5% |
Nursing and Health Professions | 6 | 2% |
Other | 26 | 10% |
Unknown | 33 | 13% |