C
Chris H. Wiggins
Researcher at Columbia University
Publications - 107
Citations - 9394
Chris H. Wiggins is an academic researcher from Columbia University. The author has contributed to research in topics: Biological network & Inference. The author has an hindex of 34, co-authored 105 publications receiving 8609 citations. Previous affiliations of Chris H. Wiggins include Kavli Institute for Theoretical Physics & Columbia University Medical Center.
Papers
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Journal ArticleDOI
ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context
Adam A. Margolin,Ilya Nemenman,Katia Basso,Chris H. Wiggins,Gustavo Stolovitzky,Riccardo Dalla Favera,Andrea Califano +6 more
TL;DR: This approach should enhance the ability to use microarray data to elucidate functional mechanisms that underlie cellular processes and to identify molecular targets of pharmacological compounds in mammalian cellular networks.
Journal ArticleDOI
ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context
Adam A. Margolin,Ilya Nemenman,Katia Basso,Ulf Klein,Chris H. Wiggins,Gustavo Stolovitzky,Riccardo Dalla Favera,Andrea Califano +7 more
TL;DR: ARACNE as mentioned in this paper uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods, and shows that it is possible to reconstruct the network exactly (asymptotically) if the effect of loops in the network topology is negligible.
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Learning Rates and States from Biophysical Time Series: A Bayesian Approach to Model Selection and Single-Molecule FRET Data
TL;DR: It is demonstrated how model selection in such probabilistic or generative modeling can facilitate analysis of closely related temporal data currently prevalent in biophysics, and how this technique can be applied to temporal data such as smFRET time series.
Journal ArticleDOI
Bayesian Approach to Network Modularity
Jake M. Hofman,Chris H. Wiggins +1 more
TL;DR: In this article, the authors present an efficient, principled, and interpretable technique for inferring module assignments and for identifying the optimal number of modules in a given network, based on Bayesian methods for model selection which have been used with success for almost a century.
Journal ArticleDOI
Opposing Effects of PKCθ and WASp on Symmetry Breaking and Relocation of the Immunological Synapse
Tasha N. Sims,Timothy J. Soos,Harry S. Xenias,Benjamin J. Dubin-Thaler,Jake M. Hofman,Janelle Waite,Thomas O. Cameron,V. Kaye Thomas,Rajat Varma,Chris H. Wiggins,Michael P. Sheetz,Dan R. Littman,Michael L. Dustin +12 more
TL;DR: It is found that IS formation during antigen recognition comprises cycles of stable IS formation and autonomous naive T cell migration and opposing effects of PKCtheta and WASp control IS stability through pSMAC symmetry breaking and reformation.