C. Wiggins

发表

Optimal Signal Processing in Small Stochastic Biochemical Networks

I. Nemenman, E. Ziv, C. Wiggins, 2006, PloS one.

Inferring network mechanisms: the Drosophila melanogaster protein interaction network.

E. Ziv, C. Wiggins, M. Middendorf, 2004, Proceedings of the National Academy of Sciences of the United States of America.

Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data

Frank D. Wood, Chris Wiggins, Jan-Willem van de Meent, 2013, ICML.

Allosteric collaboration between elongation factor G and the ribosomal L1 stalk directs tRNA movements during translation

Jake M. Hofman, Jonathan E. Bronson, C. Wiggins, 2009, Proceedings of the National Academy of Sciences.

Information-theoretic approach to network modularity.

E. Ziv, C. Wiggins, M. Middendorf, 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

Graphical models for inferring single molecule dynamics

Chris Wiggins, Jake M. Hofman, Ruben L. Gonzalez, 2010, BMC Bioinformatics.

Information-optimal transcriptional response to oscillatory driving.

A. Walczak, C. Wiggins, A. Mugler, 2010, Physical review letters.

Spectral solutions to stochastic models of gene expression with bursts and regulation.

A. Walczak, C. Wiggins, A. Mugler, 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

Binary transcriptional control of pattern formation in development

Hernan G. Garcia, C. Wiggins, Nicholas C. Lammers, 2018, bioRxiv.

Combining sequence and time series expression data to learn transcriptional modules

Anshul Kundaje, Feng Gao, Manuel Middendorf, 2005, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

A stochastic spectral analysis of transcriptional regulatory cascades

Andrew Mugler, Aleksandra M Walczak, Chris H Wiggins, 2008, Proceedings of the National Academy of Sciences.

Nonmuscle myosin IIA-dependent force inhibits cell spreading and drives F-actin flow.

Jake M. Hofman, C. Wiggins, J. Hofman, 2006, Biophysical journal.

Trapping and wiggling: elastohydrodynamics of driven microfilaments.

C. Wiggins, D. Riveline, R. Goldstein, 1997, Biophysical journal.

Multimodal transcriptional control of pattern formation in embryonic development

Hernan G. Garcia, C. Wiggins, Nicholas C. Lammers, 2019, Proceedings of the National Academy of Sciences.

Stylistic Clusters and the Syrian/South Syrian Tradition of First-Millennium BCE Levantine Ivory Carving: A Machine Learning Approach

Jan-Willem van de Meent, Amy Rebecca Gansell, Chris H. Wiggins, 2014, 1401.0871.

FLEXIVE AND PROPULSIVE DYNAMICS OF ELASTICA AT LOW REYNOLDS NUMBER

Raymond E. Goldstein, Chris H. Wiggins, C. Wiggins, 1997, cond-mat/9707346.

COMPUTATIONAL BIOLOGY AND BIOINFORMATICS

Cigdem Demir, Alexander Schliep, John Hawkins, 2005 .

Multiple LacI-mediated loops revealed by Bayesian statistics and tethered particle motion

Rob Phillips, Chris H Wiggins, Martin Lindén, 2014, Nucleic acids research.

Integrative analysis of T cell motility from multi-channel microscopy data using TIAM

W. Neiswanger, C. Wiggins, Michael Loran Dustin, 2015, Journal of immunological methods.

Identifying Hosts of Families of Viruses: A Machine Learning Approach

Michaela Dewar, C. Wiggins, R. Rabadán, 2011, PloS one.

Nonparametric Gaussian Mixture Models for the Multi-Armed Bandit

C. Wiggins, Iñigo Urteaga, 2018, 1808.02932.

Predicting Regional Classification of Levantine Ivory Sculptures: A Machine Learning Approach

Aleks Jakulin, C. Wiggins, A. Gansell, 2008, 0806.4642.

Quantification of Cell Edge Velocities and Traction Forces Reveals Distinct Motility Modules during Cell Spreading

Michael P. Sheetz, Jake M. Hofman, Anna V. Shneidman, 2008, PloS one.

The small GTPase R-Ras regulates organization of actin and drives membrane protrusions through the activity of PLCϵ

C. Wiggins, J. Hofman, M. Sheetz, 2006, Journal of Cell Science.

Trapping and wiggling: elastohydrodynamics of driven microfilaments.

C. Wiggins, D. Riveline, R. Goldstein, 1997, Biophysical journal.

VISCOUS NONLINEAR DYNAMICS OF TWIST AND WRITHE

C. Wiggins, T. Powers, R. Goldstein, 1998, cond-mat/9802084.

Durable Interactions of T Cells with T Cell Receptor Stimuli in the Absence of a Stable Immunological Synapse

W. Neiswanger, C. Wiggins, Michael Loran Dustin, 2018, Cell reports.

A Computational Study of Mixing Microchannel Flows

C. Wiggins, J. Bennett, 2003, cond-mat/0307482.

Sequential Monte Carlo for Dynamic Softmax Bandits

C. Wiggins, Iñigo Urteaga, 2018 .

Single-molecule dataset (SMD): a generalized storage format for raw and processed single-molecule data

Jan-Willem van de Meent, C. Wiggins, D. Herschlag, 2015, BMC Bioinformatics.

F eb 2 01 4 Multiple Lac-mediated loops revealed by Bayesian statistics and tethered particle motion

Jan-Willem van de Meent, Stephanie L. Johnson, C. Wiggins, 2014 .

Statistical method for revealing form-function relations in biological networks

Andrew Mugler, Chris H Wiggins, Boris Grinshpun, 2010, Proceedings of the National Academy of Sciences.

Process pathway inference via time series analysis

Chris Wiggins, Ilya Nemenman, I. Nemenman, 2002, physics/0206031.

Opposing Effects of PKCθ and WASp on Symmetry Breaking and Relocation of the Immunological Synapse

Jake M. Hofman, C. Wiggins, J. Hofman, 2007, Cell.

Fast dynamics of supercoiled DNA revealed by single-molecule experiments

Chris H Wiggins, Ralf Seidel, Aurélien Crut, 2007, Proceedings of the National Academy of Sciences.

Bayesian approach to network modularity.

Chris H Wiggins, Jake M Hofman, Jake M. Hofman, 2007, Physical review letters.

ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context

Chris Wiggins, Ilya Nemenman, Andrea Califano, 2004, BMC Bioinformatics.

Dynamics of semiflexible polymers in a flow field.

C. Wiggins, O. Hallatschek, Erwin Frey, 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

Relaxation dynamics of semiflexible polymers.

C. Wiggins, R. Granek, M. Feingold, 2004, Physical review letters.

Pegasus: a comprehensive annotation and prediction tool for detection of driver gene fusions in cancer

Chris Wiggins, Andrea Acquaviva, Elisa Ficarra, 2014, BMC Systems Biology.

Dynamic patterns and self-knotting of a driven hanging chain.

C H Wiggins, M. Shelley, C. Wiggins, 2001, Physical review letters.

Learning Regulatory Programs That Accurately Predict Differential Expression with MEDUSA

A. Kundaje, C. Leslie, C. Wiggins, 2007, Annals of the New York Academy of Sciences.

Elastohydrodynamic study of actin filaments using fluorescence microscopy

C. Wiggins, D. Riveline, R. Goldstein, 1997, cond-mat/9704225.

Learning rates and states from biophysical time series: a Bayesian approach to model selection and single-molecule FRET data.

Chris H Wiggins, Jake M Hofman, Jake M. Hofman, 2009, Biophysical journal.

Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data

C. Wiggins, Iñigo Urteaga, V. Vitzthum, 2019, npj Digital Medicine.

Learning probabilistic phenotypes from heterogeneous EHR data

Adler J. Perotte, Noémie Elhadad, Chris Wiggins, 2015, J. Biomed. Informatics.

Empirical Bayes methods enable advanced population-level analyses of single-molecule FRET experiments.

Chris H Wiggins, Jonathan E. Bronson, Jonathan E Bronson, 2014, Biophysical journal.

Learning “graph-mer” Motifs that Predict Gene Expression Trajectories in Development

Xuejing Li, Chris Wiggins, Christina S. Leslie, 2010, PLoS Comput. Biol..

Benchmarking of Dynamic Bayesian Networks Inferred from Stochastic Time‐Series Data

Chris H Wiggins, Lawrence A David, C. Wiggins, 2007, Annals of the New York Academy of Sciences.

Noise Expands the Response Range of the Bacillus subtilis Competence Circuit

Aleksandra M. Walczak, Chris Wiggins, Andrew Mugler, 2015, PLoS Comput. Biol..

Quantifying evolvability in small biological networks.

A Mugler, E Ziv, I Nemenman, 2008, IET systems biology.

Analytic methods for modeling stochastic regulatory networks.

Andrew Mugler, Aleksandra M Walczak, Chris H Wiggins, 2010, Methods in molecular biology.

Systematic identification of statistically significant network measures.

Manuel Middendorf, Etay Ziv, Robin Koytcheff, 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

Discriminative topological features reveal biological network mechanisms

Chris Wiggins, Manuel Middendorf, Etay Ziv, 2004, BMC Bioinformatics.

Dose-response modeling in high-throughput cancer drug screenings: an end-to-end approach.

Scott W. Linderman, Haoran Zhang, Chris H Wiggins, 2018, Biostatistics.

Dose-response modeling in high-throughput cancer drug screenings: A case study with recommendations for practitioners

Scott W. Linderman, Haoran Zhang, David M. Blei, 2018, 1812.05691.

The stochastic spectral dynamics of bending and tumbling

C. Wiggins, M. Pasquali, A. Montesi, 2003, cond-mat/0307551.

Biopolymer mechanics: stability, dynamics, and statistics

C. Wiggins, 2001 .

Variational inference for the multi-armed contextual bandit pdf

Iñigo Urteaga, Chris Wiggins, C. Wiggins, 2017, AISTATS.

Bayesian bandits: balancing the exploration-exploitation tradeoff via double sampling pdf

Iñigo Urteaga, Chris Wiggins, C. Wiggins, 2017, ArXiv.

Nonparametric Gaussian mixture models for the multi-armed contextual bandit pdf

Iñigo Urteaga, Chris Wiggins, C. Wiggins, 2018, ArXiv.

A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking

Iñigo Urteaga, Chris H Wiggins, Noémie Elhadad, 2021, J. Am. Medical Informatics Assoc..

A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health data pdf

Virginia J. Vitzthum, Chris H. Wiggins, Inigo Urteaga, 2021, ArXiv.

(Sequential) Importance Sampling Bandits pdf

Iñigo Urteaga, Chris Wiggins, C. Wiggins, 2018, ArXiv.

Predicting delayed cerebral ischemia after subarachnoid hemorrhage using physiological time series data

Edouard Grave, Jan Claassen, Murad Megjhani, 2019, Journal of Clinical Monitoring and Computing.

Predicting genetic regulatory response using classification

Yoav Freund, Chris Wiggins, Anshul Kundaje, 2004, ISMB/ECCB.

A Generative Modeling Approach to Calibrated Predictions: A Use Case on Menstrual Cycle Length Prediction

C. Wiggins, Iñigo Urteaga, V. Vitzthum, 2021, MLHC.

Lateral membrane waves constitute a universal dynamic pattern of motile cells.

Chris H Wiggins, Jake M Hofman, Jake M. Hofman, 2006, Physical review letters.

Magma migration and magmatic solitary waves in 3-D

Chris H. Wiggins, Marc Spiegelman, C. Wiggins, 1995 .

Inference in Hidden Markov Models with Explicit State Duration Distributions

Frank D. Wood, Chris Wiggins, Michael Dewar, 2012, IEEE Signal Processing Letters.

Quantification of Cell Movement Reveals Distinct Edge Motility Types During Cell Spreading

Jake M. Hofman, C. Wiggins, J. Hofman, 2007 .

Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data.

C. Wiggins, Iñigo Urteaga, V. Vitzthum, 2020, NPJ digital medicine.

Time-dependent information transmission in a model regulatory circuit.

M Marsili, C H Wiggins, F Mancini, 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

Novel systematic discovery of statistically significant network features

Etay Ziv, Robin Koytcheff, Chris H. Wiggins, 2003 .

A classification-based framework for predicting and analyzing gene regulatory response

Yoav Freund, Chris Wiggins, Anshul Kundaje, 2006, BMC Bioinformatics.

A Bayesian framework for community detection in networks

Jake M. Hofman, C. Wiggins, J. Hofman, 2007 .

Discriminative topological features reveal biological network mechanisms

E. Ziv, C. Wiggins, C. Levovitz, 2004, BMC Bioinformatics.

Analytic methods for modeling stochastic regulatory networks.

A. Walczak, C. Wiggins, A. Mugler, 2010, Methods in molecular biology.

Single-molecule dataset (SMD): a generalized storage format for raw and processed single-molecule data

Chris Wiggins, Daniel Herschlag, Jan-Willem van de Meent, 2015, BMC Bioinformatics.

Empirical Bayes methods enable advanced population-level analyses of single-molecule FRET experiments.

Jonathan E. Bronson, C. Wiggins, Jan-Willem van de Meent, 2014, Biophysical journal.

Motif Discovery Through Predictive Modeling of Gene Regulation

Yoav Freund, Chris Wiggins, Anshul Kundaje, 2005, RECOMB.

Predicting Genetic Regulatory Response Using Classification: Yeast Stress Response

Yoav Freund, Chris Wiggins, Anshul Kundaje, 2004, Regulatory Genomics.

An Information-Theoretic Derivation of Min-Cut-Based Clustering

Chris Wiggins, Anil Raj, C. Wiggins, 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Nonparametric Gaussian Mixture Models for the Multi-Armed Bandit

C. Wiggins, Iñigo Urteaga, 2018, 1808.02932.