Computational Method Finds Gene-to-Gene, Drug-to-Drug Connections

By mining large datasets, researchers can better understand gene-gene, protein-protein, and drug/side-effect interactions.  Two recent publications in the journals BMC Bioinformatics and BMC Systems Biology, from researchers at the Mount Sinai School of Medicine, describe computational methods that enable scientists to identify and prioritize genes, drug targets, and strategies for repositioning drugs that are already on the market. The algorithms also scientists identify fellow researchers with whom they can collaborate.

The Mount Sinai team analyzed 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 also connected 53 cancer drugs to 32 severe side effects. For example, when chemotherapy was combined with cancer drugs that work through cell signaling, there was a strong link to cardiovascular related adverse events. Findings of this sort can assist in post-marketing surveillance safety of approved drugs.

Read More at Mount Sinai Medical Center 

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