David Fenyo

ORCID iD
https://orcid.org/0000-0001-5049-3825
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United States

Sources:
David Fenyo (2016-01-24)

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http://fenyolab.org

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David Fenyo (2015-12-18)

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Scopus Author ID: 6603891585

Sources:
Scopus to ORCID (2015-12-18)

Biography

David Fenyö studied engineering physics, with a focus on mathematical and numerical methods, at Uppsala University in Sweden. After receiving an M.Sc. in 1987, he joined the laboratory of Dr. Bo Sundqvist at Uppsala University, and studied the mechanisms of ion–solid interaction both experimentally, and using molecular dynamics and Monte Carlo simulations. For this work, he received a Ph.D. in Physics in 1991. He then joined the laboratory of Dr. Brian Chait at the Rockefeller University, where he started developing algorithms to analyze proteomic data obtained using mass spectrometry. In 1997 he co-founded ProteoMetrics, a bioinformatics startup that sought to commercialize these algorithms. He served as the president of Proteomitrics and created several software packages, including a distributed software system for fully automated analysis of large-scale proteomics data. Subsequently, he worked for several companies including GE Healthcare before returning to academia. During these years, he laid a statistical foundation to test the significance of protein identification results, developed search engines that identify proteins by matching mass spectrometric and sequence data, and built commercial software packages for fully automated high-throughput identification and quantitation of proteins. David Fenyo joined the faculty of NYU Medical School in 2010 and currently his research focuses on providing a detailed understanding of the dynamics of cellular processes. He applies mathematical, statistical, and computational methods to optimize experimental design, analyze quantitative data, and model biological systems. In particular, he uses proteomic approaches to develop methods to identify, characterize, and quantify proteins. His efforts to integrate data from multiple technologies—including mass spectrometry, sequencing, and microscopy—have provided a wide array of powerful tools for discover and verify biomarkers and therapeutic targets in cancer.
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