S
Soheil Feizi
Researcher at University of Maryland, College Park
Publications - 172
Citations - 9418
Soheil Feizi is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Computer science & Robustness (computer science). The author has an hindex of 28, co-authored 132 publications receiving 7281 citations. Previous affiliations of Soheil Feizi include Stanford University & IBM.
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Journal ArticleDOI
Integrative analysis of 111 reference human epigenomes
Anshul Kundaje,Wouter Meuleman,Wouter Meuleman,Jason Ernst,Misha Bilenky,Angela Yen,Angela Yen,Alireza Heravi-Moussavi,Pouya Kheradpour,Pouya Kheradpour,Zhizhuo Zhang,Zhizhuo Zhang,Jianrong Wang,Jianrong Wang,Michael J. Ziller,Viren Amin,John W. Whitaker,Matthew D. Schultz,Lucas D. Ward,Lucas D. Ward,Abhishek Sarkar,Abhishek Sarkar,Gerald Quon,Gerald Quon,Richard Sandstrom,Matthew L. Eaton,Matthew L. Eaton,Yi-Chieh Wu,Yi-Chieh Wu,Andreas R. Pfenning,Andreas R. Pfenning,Xinchen Wang,Xinchen Wang,Melina Claussnitzer,Melina Claussnitzer,Yaping Liu,Yaping Liu,Cristian Coarfa,R. Alan Harris,Noam Shoresh,Charles B. Epstein,Elizabeta Gjoneska,Elizabeta Gjoneska,Danny Leung,Wei Xie,R. David Hawkins,Ryan Lister,Chibo Hong,Philippe Gascard,Andrew J. Mungall,Richard A. Moore,Eric Chuah,Angela Tam,Theresa K. Canfield,R. Scott Hansen,Rajinder Kaul,Peter J. Sabo,Mukul S. Bansal,Mukul S. Bansal,Mukul S. Bansal,Annaick Carles,Jesse R. Dixon,Kai How Farh,Soheil Feizi,Soheil Feizi,Rosa Karlic,Ah Ram Kim,Ah Ram Kim,Ashwinikumar Kulkarni,Daofeng Li,Rebecca F. Lowdon,Ginell Elliott,Tim R. Mercer,Shane Neph,Vitor Onuchic,Paz Polak,Paz Polak,Nisha Rajagopal,Pradipta R. Ray,Richard C Sallari,Richard C Sallari,Kyle Siebenthall,Nicholas A Sinnott-Armstrong,Nicholas A Sinnott-Armstrong,Michael Stevens,Robert E. Thurman,Jie Wu,Bo Zhang,Xin Zhou,Arthur E. Beaudet,Laurie A. Boyer,Philip L. De Jager,Philip L. De Jager,Peggy J. Farnham,Susan J. Fisher,David Haussler,Steven J.M. Jones,Steven J.M. Jones,Wei Li,Marco A. Marra,Michael T. McManus,Shamil R. Sunyaev,Shamil R. Sunyaev,James A. Thomson,Thea D. Tlsty,Li-Huei Tsai,Li-Huei Tsai,Wei Wang,Robert A. Waterland,Michael Q. Zhang,Lisa Helbling Chadwick,Bradley E. Bernstein,Bradley E. Bernstein,Bradley E. Bernstein,Joseph F. Costello,Joseph R. Ecker,Martin Hirst,Alexander Meissner,Aleksandar Milosavljevic,Bing Ren,John A. Stamatoyannopoulos,Ting Wang,Manolis Kellis,Manolis Kellis +123 more
TL;DR: It is shown that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease.
Journal ArticleDOI
Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay
Alexandre Melnikov,Anand Murugan,Xiaolan Zhang,Tiberiu Tesileanu,Tiberiu Tesileanu,Li Wang,Peter Rogov,Soheil Feizi,Soheil Feizi,Andreas Gnirke,Curtis G. Callan,Curtis G. Callan,Justin B. Kinney,Manolis Kellis,Manolis Kellis,Eric S. Lander,Eric S. Lander,Eric S. Lander,Tarjei S. Mikkelsen,Tarjei S. Mikkelsen +19 more
TL;DR: A massively parallel reporter assay (MPRA) that facilitates the systematic dissection of transcriptional regulatory elements and QSAMs from two cellular states can be combined to design enhancer variants that optimize potentially conflicting objectives, such as maximizing induced activity while minimizing basal activity.
Rapid dissection and model-based optimization of inducible enhancers in human cells using a massively parallel reporter assay
Alexandre Melnikov,Anand Murugan,Xiaolan Zhang,Tiberiu Tesileanu,Li Wang,Peter Rogov,Soheil Feizi,Andreas Gnirke,Curtis G. Callan,Justin B. Kinney,Manolis Kellis,Eric S. Lander,Tarjei S. Mikkelsen +12 more
Journal ArticleDOI
Network deconvolution as a general method to distinguish direct dependencies in networks
Soheil Feizi,Soheil Feizi,Daniel Marbach,Daniel Marbach,Muriel Medard,Manolis Kellis,Manolis Kellis +6 more
TL;DR: This work presents a general method for inferring direct effects from an observed correlation matrix containing both direct and indirect effects, and introduces an algorithm that removes the combined effect of all indirect paths of arbitrary length in a closed-form solution by exploiting eigen-decomposition and infinite-series sums.
Network deconvolution as a general method to distinguish direct dependencies in networks
Soheil Feizi,Soheil Feizi,Daniel Marbach,Daniel Marbach,Muriel Medard,Manolis Kellis,Manolis Kellis +6 more
TL;DR: In this paper, a general method for inferring direct effects from an observed correlation matrix containing both direct and indirect effects is presented, which is the inverse of network convolution, and introduces an algorithm that removes the combined effect of all indirect paths of arbitrary length in a closed-form solution by exploiting eigendecomposition and infinite-series sums.