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Mount Sinai Invests $200M Into New Precision Medicine Data Science Program

NEW YORK (GenomeWeb) – Mouth Sinai’s Icahn School of Medicine said today that it is investing $200 million into a new program to fast-track drug discoveries through the integration of large-scale data analysis and genomic technologies.

The school also said it has recruited Adam Margolin, former director of computational biology at Oregon Health & Science University, to lead the initiative. Margolin will replace Eric Schadt, who has been leading Mount Sinai's data science and genomics projects. Schadt will continue at Mount Sinai in his role as dean for precision medicine at the school of medicine.

Margolin is a leader in the development of machine-learning algorithms to analyze large-scale datasets that predict which patients will response to treatments. He has also developed software systems to enable collaborative analysis within national and international projects in cancer, genomics, cancer immunotherapy, stem cell research, and pediatric diseases.

Margolin plans to continue such efforts at Mount Sinai. "We will bring together the top data scientists, genomic technology innovators, and disease experts who will work side by side every day to understand the molecular causes of complex diseases, rapidly test new therapies derived from these insights, and bring these therapies to patients faster than ever before,” he said in a statement.

In the next decade, Mount Sinai plans to hire 30 new faculty members with expertise in data science and genome technologies, and 25 data scientists who will lead projects on interpreting large-scale biomolecular data. The new program will also launch multidisciplinary projects to discover new precision medicines, build computational infrastructure that will enable data to be integrated and analyzed throughout the Mount Sinai health system, build platforms for implementing molecular profiling and therapeutic testing tools, and launch data science training programs for PhD and master's degree students.

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