ScienceDaily (July 24, 2012) Researchers from Mount Sinai School of Medicine have developed a new computational method that will make it easier for scientists to identify and prioritize genes, drug targets, and strategies for repositioning drugs that are already on the market. By mining massive datasets more simply and efficiently, researchers will be able to better comprehend gene-gene, protein-protein, and drug/side-effect interactions. The new algorithm will also help scientists identify fellow researchers with whom they can collaborate.
Led by Avi Ma’ayan, PhD, Assistant Professor of Pharmacology and Systems Therapeutics at Mount Sinai School of Medicine, and Neil Clark, PhD a postdoctoral fellow in the Ma’ayan laboratory, the team of investigators used the new algorithm to create 15 different types of gene-gene networks. They also discovered novel connections between drugs and side effects, and built a collaboration network that connected Mount Sinai investigators based on their past publications.
“The algorithm makes it easy to build networks from data,” stated Dr. Ma’ayan. “Once high dimensional and complex data is converted to networks, we can comprehend the data better and discover new and significant relationships, and focus on the important features of the data.”
The group examined one million medical records of patients to build a network that connects commonly co-prescribed drugs, commonly co-occurring side effects, and the relationships between side effects and combinations of drugs. They found that reported side effects may not be caused by the drugs, but by a separate condition of the patient that may be unrelated to the drugs. They also looked at 53 cancer drugs and connected them to 32 severe side effects. When chemotherapy was combined with cancer drugs that work through cell signaling, there was a strong link to cardiovascular related adverse events. These findings can assist in post-marketing surveillance safety of approved drugs.
The approach is presented in two separate publications in the journals BMC Bioinformatics and BMC Systems Biology. The tools that implement the approach Genes2FANs and Sets2Networks can be found on-line at http://actin.pharm.mssm.edu/genes2FANs and http://www.maayanlab.net/S2N.
Share this story on Facebook, Twitter, and Google:
Other social bookmarking and sharing tools:
The above story is reprinted from materials provided by The Mount Sinai Hospital / Mount Sinai School of Medicine, via EurekAlert!, a service of AAAS.
Note: Materials may be edited for content and length. For further information, please contact the source cited above.
Note: If no author is given, the source is cited instead.
Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.
- Malaria vaccine reduced severe and uncomplicated malaria by a third in young infants when given with other routine vaccines
- Organized medicine unveils plan to overhaul Medicare delivery
- Diet, Diabetes, and Doubt: Is Preventive Medicine Lost in Space?
- Herb Extract Helps Prevent Colds
- Will a “silent exodus” from medicine worsen physician shortage?
- Patients often seek general medical care from specialists
- Increased risk of heart failure and cardiovascular death linked to thickening of heart’s right ventricle
- Doping With Muscle-Building Drugs: FAQ
- How Much Vitamin D?
- Residents willing to work when they’re sick
Submited at Wednesday, July 25th, 2012 at 8:15 am on Uncategorized by ethan
Comment RSS 2.0 - leave a comment - trackback