Title |
DIVAN: accurate identification of non-coding disease-specific risk variants using multi-omics profiles
|
---|---|
Published in |
Genome Biology, December 2016
|
DOI | 10.1186/s13059-016-1112-z |
Pubmed ID | |
Authors |
Li Chen, Peng Jin, Zhaohui S. Qin |
Abstract |
Understanding the link between non-coding sequence variants, identified in genome-wide association studies, and the pathophysiology of complex diseases remains challenging due to a lack of annotations in non-coding regions. To overcome this, we developed DIVAN, a novel feature selection and ensemble learning framework, which identifies disease-specific risk variants by leveraging a comprehensive collection of genome-wide epigenomic profiles across cell types and factors, along with other static genomic features. DIVAN accurately and robustly recognizes non-coding disease-specific risk variants under multiple testing scenarios; among all the features, histone marks, especially those marks associated with repressed chromatin, are often more informative than others. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 29% |
France | 2 | 14% |
United Kingdom | 2 | 14% |
Denmark | 1 | 7% |
Italy | 1 | 7% |
Unknown | 4 | 29% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 11 | 79% |
Members of the public | 2 | 14% |
Science communicators (journalists, bloggers, editors) | 1 | 7% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 1% |
Italy | 1 | 1% |
Unknown | 82 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 20 | 24% |
Researcher | 19 | 23% |
Professor > Associate Professor | 5 | 6% |
Student > Master | 5 | 6% |
Student > Doctoral Student | 4 | 5% |
Other | 14 | 17% |
Unknown | 17 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 29 | 35% |
Agricultural and Biological Sciences | 10 | 12% |
Computer Science | 10 | 12% |
Medicine and Dentistry | 4 | 5% |
Neuroscience | 4 | 5% |
Other | 8 | 10% |
Unknown | 19 | 23% |