| Title |
Pathway Analyses and Understanding Disease Associations
|
|---|---|
| Published in |
Current Genetic Medicine Reports, September 2013
|
| DOI | 10.1007/s40142-013-0025-3 |
| Pubmed ID | |
| Authors |
Yu Liu, Mark R. Chance |
| Abstract |
High throughput technologies have been applied to investigate the underlying mechanisms of complex diseases, identify disease-associations and help to improve treatment. However it is challenging to derive biological insight from conventional single gene based analysis of "omics" data from high throughput experiments due to sample and patient heterogeneity. To address these challenges, many novel pathway and network based approaches were developed to integrate various "omics" data, such as gene expression, copy number alteration, Genome Wide Association Studies, and interaction data. This review will cover recent methodological developments in pathway analysis for the detection of dysregulated interactions and disease-associated subnetworks, prioritization of candidate disease genes, and disease classifications. For each application, we will also discuss the associated challenges and potential future directions. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 2% |
| Switzerland | 1 | 2% |
| Malaysia | 1 | 2% |
| Korea, Republic of | 1 | 2% |
| India | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 37 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 35% |
| Researcher | 9 | 21% |
| Student > Master | 4 | 9% |
| Professor > Associate Professor | 3 | 7% |
| Professor | 2 | 5% |
| Other | 4 | 9% |
| Unknown | 6 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 15 | 35% |
| Biochemistry, Genetics and Molecular Biology | 7 | 16% |
| Computer Science | 4 | 9% |
| Mathematics | 2 | 5% |
| Immunology and Microbiology | 1 | 2% |
| Other | 5 | 12% |
| Unknown | 9 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 October 2013.
All research outputs
#18,351,676
of 22,727,570 outputs
Outputs from Current Genetic Medicine Reports
#85
of 115 outputs
Outputs of similar age
#152,067
of 204,189 outputs
Outputs of similar age from Current Genetic Medicine Reports
#4
of 4 outputs
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