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Daniel K. Shenfeld
Researcher at Columbia University
Publications - 10
Citations - 2583
Daniel K. Shenfeld is an academic researcher from Columbia University. The author has contributed to research in topics: Quantum cohomology & Equivariant map. The author has an hindex of 8, co-authored 10 publications receiving 2245 citations. Previous affiliations of Daniel K. Shenfeld include Princeton University & D. E. Shaw Research.
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Journal ArticleDOI
viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia
El-ad David Amir,Kara L. Davis,Michelle D. Tadmor,Erin F. Simonds,Jacob H. Levine,Sean C. Bendall,Daniel K. Shenfeld,Smita Krishnaswamy,Garry P. Nolan,Dana Pe'er +9 more
TL;DR: In this article, the authors present viSNE, a tool that allows one to map high-dimensional cytometry data onto two dimensions, yet conserve the highdimensional structure of the data by using all pairwise distances in high dimension to determine each cell's location in the plot.
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Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development
Sean C. Bendall,Kara L. Davis,El-ad David Amir,Michelle D. Tadmor,Erin F. Simonds,Tiffany J. Chen,Daniel K. Shenfeld,Garry P. Nolan,Dana Pe'er +8 more
TL;DR: This study provides a comprehensive analysis of human B lymphopoiesis, laying a foundation to apply this approach to other tissues and "corrupted" developmental processes including cancer.
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Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes: An Application of Machine Learning Using Electronic Health Records
Jeffrey P. Anderson,Jignesh R. Parikh,Daniel K. Shenfeld,Vladimir Ivanov,Casey Marks,Bruce W. Church,Jason M. Laramie,Jack Mardekian,Beth Anne Piper,Richard J. Willke,Dale Rublee +10 more
TL;DR: Identification of established risk factors for T2D serves as proof of concept for this analytical approach, while novel factors selected by REFS represent emerging areas of T1D research.
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Minimizing thermodynamic length to select intermediate states for free-energy calculations and replica-exchange simulations.
TL;DR: This work derives bounds for quantities in terms of the thermodynamic distance between the intermediates in two cases where the choice of intermediate states is particularly important: minimizing statistical error in free-energy difference calculations and maximizing average acceptance probabilities in replica-exchange simulations.
Journal Article
Publisher's Note: Minimizing thermodynamic length to select intermediate states for free-energy calculations and replica-exchange simulations
TL;DR: In this article, the authors consider two cases where the choice of intermediate states is particularly important: minimizing statistical error in free-energy difference calculations and maximizing average acceptance probabilities in replica-exchange simulations.