Title |
DENSE: efficient and prior knowledge-driven discovery of phenotype-associated protein functional modules
|
---|---|
Published in |
BMC Systems Biology, October 2011
|
DOI | 10.1186/1752-0509-5-172 |
Pubmed ID | |
Authors |
Willam Hendrix, Andrea M Rocha, Kanchana Padmanabhan, Alok Choudhary, Kathleen Scott, James R Mihelcic, Nagiza F Samatova |
Abstract |
Identifying cellular subsystems that are involved in the expression of a target phenotype has been a very active research area for the past several years. In this paper, cellular subsystem refers to a group of genes (or proteins) that interact and carry out a common function in the cell. Most studies identify genes associated with a phenotype on the basis of some statistical bias, others have extended these statistical methods to analyze functional modules and biological pathways for phenotype-relatedness. However, a biologist might often have a specific question in mind while performing such analysis and most of the resulting subsystems obtained by the existing methods might be largely irrelevant to the question in hand. Arguably, it would be valuable to incorporate biologist's knowledge about the phenotype into the algorithm. This way, it is anticipated that the resulting subsytems would not only be related to the target phenotype but also contain information that the biologist is likely to be interested in. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 2 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 7% |
Hungary | 1 | 2% |
Germany | 1 | 2% |
Unknown | 38 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 9 | 21% |
Researcher | 4 | 9% |
Student > Master | 2 | 5% |
Professor | 1 | 2% |
Other | 1 | 2% |
Other | 2 | 5% |
Unknown | 24 | 56% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 10 | 23% |
Computer Science | 3 | 7% |
Biochemistry, Genetics and Molecular Biology | 2 | 5% |
Immunology and Microbiology | 1 | 2% |
Medicine and Dentistry | 1 | 2% |
Other | 0 | 0% |
Unknown | 26 | 60% |