Title |
Identification of novel therapeutics for complex diseases from genome-wide association data
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Published in |
BMC Medical Genomics, May 2014
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DOI | 10.1186/1755-8794-7-s1-s8 |
Pubmed ID | |
Authors |
Mani P Grover, Sara Ballouz, Kaavya A Mohanasundaram, Richard A George, Craig D H Sherman, Tamsyn M Crowley, Merridee A Wouters |
Abstract |
Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Unknown | 2 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 3% |
United Kingdom | 1 | 1% |
Canada | 1 | 1% |
Brazil | 1 | 1% |
Unknown | 93 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 15 | 15% |
Student > Ph. D. Student | 13 | 13% |
Researcher | 13 | 13% |
Student > Master | 12 | 12% |
Other | 10 | 10% |
Other | 20 | 20% |
Unknown | 16 | 16% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 23 | 23% |
Agricultural and Biological Sciences | 21 | 21% |
Biochemistry, Genetics and Molecular Biology | 8 | 8% |
Psychology | 6 | 6% |
Computer Science | 5 | 5% |
Other | 16 | 16% |
Unknown | 20 | 20% |