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Eyal Mozes
Researcher at Columbia University
Publications - 7
Citations - 966
Eyal Mozes is an academic researcher from Columbia University. The author has contributed to research in topics: Cancer & Genome. The author has an hindex of 5, co-authored 6 publications receiving 931 citations. Previous affiliations of Eyal Mozes include Stanford University.
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An integrated approach to uncover drivers of cancer.
Uri David Akavia,Oren Litvin,Jessica J. Kim,Jessica J. Kim,Felix Sanchez-Garcia,Dylan Kotliar,Helen C. Causton,Panisa Pochanard,Panisa Pochanard,Eyal Mozes,Levi A. Garraway,Levi A. Garraway,Dana Pe'er +12 more
TL;DR: A computational framework is developed that integrates chromosomal copy number and gene expression data for detecting aberrations that promote cancer progression and correctly identified known drivers of melanoma and predicted multiple tumor dependencies.
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High speed and robust event correlation
TL;DR: The authors describe a network management system and illustrates its application to managing a distributed database application on a complex enterprise network.
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JISTIC: Identification of Significant Targets in Cancer
TL;DR: JISTIC is an easy-to-install platform independent implementation of GISTIC that outperforms the original algorithm detecting more relevant candidate genes and regions and is an improvement over the widely used GISTic algorithm.
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A deductive database based on aristotelian logic
TL;DR: A new type of deductive database is proposed, whose structure is based on Aristotle's rules of the syllogism, which demonstrates the advantages of Aristotelian logic over more modern formalisms for applications in which natural and informative interaction with human users is important.
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Create and assess protein networks through molecular characteristics of individual proteins
Yanay Ofran,Guy Yachdav,Guy Yachdav,Eyal Mozes,Ta-tsen Soong,Rajesh Nair,Burkhard Rost,Burkhard Rost +7 more
TL;DR: A novel platform for data integration that generates networks on the macro system-level, analyzes the molecular characteristics of each protein on the micro level, and then combines the two levels by using the molecular characteristic to assess networks is introduced.