Researchers have developed a new computational method that will make it easier for scientists to identify and prioritize genes, drug targets, and strategies. The method would help in repositioning drugs that are already on the market. The researchers are from Mount Sinai School of Medicine. Researchers on mining large datasets more simply and efficiently, will be able to better understand 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 simple to build networks from data," said Dr. Ma'ayan. "Once high dimensional and complex data is converted to networks, we can understand the data better and discover new and significant relationships, and focus on the important features of the data."