Title |
FEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approach
|
---|---|
Published in |
BMC Bioinformatics, November 2011
|
DOI | 10.1186/1471-2105-12-s12-s3 |
Pubmed ID | |
Authors |
Bing Han, Xue-wen Chen, Zohreh Talebizadeh |
Abstract |
The interactions among genetic factors related to diseases are called epistasis. With the availability of genotyped data from genome-wide association studies, it is now possible to computationally unravel epistasis related to the susceptibility to common complex human diseases such as asthma, diabetes, and hypertension. However, the difficulties of detecting epistatic interaction arose from the large number of genetic factors and the enormous size of possible combinations of genetic factors. Most computational methods to detect epistatic interactions are predictor-based methods and can not find true causal factor elements. Moreover, they are both time-consuming and sample-consuming. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Australia | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 6% |
Unknown | 34 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 12 | 33% |
Researcher | 7 | 19% |
Student > Master | 3 | 8% |
Student > Doctoral Student | 1 | 3% |
Student > Bachelor | 1 | 3% |
Other | 4 | 11% |
Unknown | 8 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 9 | 25% |
Engineering | 5 | 14% |
Computer Science | 5 | 14% |
Biochemistry, Genetics and Molecular Biology | 3 | 8% |
Mathematics | 2 | 6% |
Other | 4 | 11% |
Unknown | 8 | 22% |