D. Wild

发表

Identifying Protein Complexes in High-Throughput Protein Interaction Screens Using an Infinite Latent Feature Model

Wei Chu, Zoubin Ghahramani, David L. Wild, 2005, Pacific Symposium on Biocomputing.

Clustering Protein Sequence and Structure Space with Infinite Gaussian Mixture Models

Carl E. Rasmussen, Zoubin Ghahramani, David L. Wild, 2003, Pacific Symposium on Biocomputing.

Biomarker discovery in microarray gene expression data with Gaussian processes

Wei Chu, Zoubin Ghahramani, Francesco Falciani, 2005, Bioinform..

High-Resolution Temporal Profiling of Transcripts during Arabidopsis Leaf Senescence Reveals a Distinct Chronology of Processes and Regulation[C][W][OA]

Christopher A. Penfold, Dafyd J. Jenkins, D. Wild, 2011, Plant Cell.

Arabidopsis Defense against Botrytis cinerea: Chronology and Regulation Deciphered by High-Resolution Temporal Transcriptomic Analysis[C][W][OA]

Karsten M. Borgwardt, Christopher A. Penfold, Emma J. Cooke, 2012, Plant Cell.

Transcriptional Dynamics Driving MAMP-Triggered Immunity and Pathogen Effector-Mediated Immunosuppression in Arabidopsis Leaves Following Infection with Pseudomonas syringae pv tomato DC3000[OPEN]

Christopher A. Penfold, Dafyd J. Jenkins, George R. Littlejohn, 2015, Plant Cell.

The Status of Structural Genomics Defined Through the Analysis of Current Targets and Structures

Tong Liu, Li Chen, Philip E. Bourne, 2003, Pacific Symposium on Biocomputing.

A Bayesian approach to modelling uncertainty in gene expression clusters

Zoubin Ghahramani, C. Rasmussen, D. Wild, 2002 .

A Bayesian network model for protein fold and remote homologue recognition

Zoubin Ghahramani, David L. Wild, A. Raval, 2002, Bioinform..

Discovering transcriptional modules by Bayesian data integration

Zoubin Ghahramani, Jim E. Griffin, David L. Wild, 2010, Bioinform..

Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction

Wei Chu, Zoubin Ghahramani, David L. Wild, 2006, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Reconstruction and stability of secondary structure elements in the context of protein structure prediction.

David L Wild, A. Podtelezhnikov, D. Wild, 2009, Biophysical journal.

Learning about protein hydrogen bonding by minimizing contrastive divergence

Zoubin Ghahramani, David L Wild, Zoubin Ghahramani, 2006, Proteins.

The dynamic architecture of the metabolic switch in Streptomyces coelicolor

Kay Nieselt, Rainer Breitling, Eriko Takano, 2010, BMC Genomics.

Computational approaches to the integration of gene expression, ChIP-chip and sequence data in the inference of gene regulatory networks.

Emma J. Cooke, David L Wild, D. Wild, 2009, Seminars in cell & developmental biology.

Bayesian Segmental Models with Alignment Profiles for Protein Secondary Structure Prediction

Wei Chu, D. Wild, 2004 .

A Bayesian network approach to protein fold recognition

Zoubin Ghahramani, David L. Wild, Zoubin Ghahramani, 1998 .

Baysian Segmental Models for Protein Secondary Structure and Contact Map Prediction

Wei Chu, Zoubin Ghahramani, Alexei A. Podtelezhnikov, 2006 .

Modelling biological responses using gene expression profiling and linear dynamical systems

Zoubin Ghahramani, Francesco Falciani, David L. Wild, 2001 .

HOLOGRAPHIC METHODS FOR X-RAY AND ELECTRON CRYSTALLOGRAPHY

D. Wild, D. Saldin, V. Shneerson, 1997 .

Time-Series Transcriptomics Reveals That AGAMOUS-LIKE22 Affects Primary Metabolism and Developmental Processes in Drought-Stressed Arabidopsis[CC-BY]

Christopher A. Penfold, Dafyd J. Jenkins, T. Lawson, 2016, Plant Cell.

Exploiting molecular dynamics in Nested Sampling simulations of small peptides

David L. Wild, Robert J. N. Baldock, Gábor Csányi, 2016, Comput. Phys. Commun..

Macromolecular crystallography with synchrotron radiation. II. Results

J. Jenkins, D. Stuart, L. Johnson, 1983 .

High-Resolution Temporal Proﬁling of Transcripts during Arabidopsis Leaf Senescence Reveals a Distinct Chronology of Processes and Regulation

Christopher A. Penfold, Dafyd J. Jenkins, D. Wild, 2011 .

An approach to pathway reconstruction using whole genome metabolic models and sensitive sequence searching

R. Dobson, D. Wild, M. Saqi, 2009, J. Integr. Bioinform..

Title : Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks

Christopher A. Penfold, D. Wild, K. Denby, 2012 .

Crystallographic studies on the activity of glycogen phosphorylase b

J. Jenkins, L. Johnson, D. Wild, 1978, Nature.

Inferring orthologous gene regulatory networks using interspecies data fusion

Christopher A. Penfold, David L. Wild, Jonathan B. A. Millar, 2015, Bioinform..

R/BHC: fast Bayesian hierarchical clustering for microarray data

Yang Xu, Katherine A. Heller, Zoubin Ghahramani, 2009, BMC Bioinformatics.

A local regulatory network around three NAC transcription factors in stress responses and senescence in Arabidopsis leaves

Christopher A. Penfold, Claire Hill, Emma J. Cooke, 2013, The Plant journal : for cell and molecular biology.

Time series transcriptomics reveals a BBX32-directed control of acclimation to high light in mature Arabidopsis leaves.

Christopher A. Penfold, T. Lawson, D. Wild, 2021, The Plant journal : for cell and molecular biology.

Time series transcriptomics reveals a BBX32-directed control of dynamic acclimation to high light in mature Arabidopsis leaves

Christopher A. Penfold, T. Lawson, D. Wild, 2020, bioRxiv.

Time series transcriptomics reveals a BBX 32-directed control of 1 dynamic acclimation to high light in mature Arabidopsis leaves 2 Running Title : The regulation of dynamic acclimation in Arabidopsis 3

Christopher A. Penfold, T. Lawson, D. Wild, 2020 .

Modeling T-cell activation using gene expression profiling and state-space models

Zoubin Ghahramani, Francesco Falciani, David L. Wild, 2004, Bioinform..

The relationship of the crystal structure of amylose polymorphs to the structure of the starch granule

D. Wild, J. Blanshard, 1986 .

Modeling and Visualizing Uncertainty in Gene Expression Clusters Using Dirichlet Process Mixtures

Carl E. Rasmussen, Zoubin Ghahramani, David L. Wild, 2009, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Zoubin Ghahramani, Jim E. Griffin, Paul D. W. Kirk, 2012, Bioinform..

Bringing numerous methods for expression and promoter analysis to a public cloud computing service

Bo Gao, Paul Brown, Sascha Ott, 2017, Bioinform..

Inferring Gene Regulatory Networks from Multiple Datasets.

Christopher A. Penfold, Christopher A Penfold, David L Wild, 2018, Methods in molecular biology.

A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series

Zoubin Ghahramani, Oliver Stegle, Karsten M. Borgwardt, 2009, RECOMB.

Pathway for the Management of Heart Failure Complicating Acute Coronary Syndrome

D. Wild, E. Herzog, E. Aziz, 2009 .

Exhaustive Metropolis Monte Carlo sampling and analysis of polyalanine conformations adopted under the influence of hydrogen bonds

David L Wild, A. Podtelezhnikov, D. Wild, 2005, Proteins.

Exploring the energy landscapes of protein folding simulations with Bayesian computation.

David L Wild, Csilla Várnai, Nikolas S. Burkoff, 2010, Biophysical journal.

An exponential modeling algorithm for protein structure completion by X-ray crystallography.

D. Wild, D. Saldin, V. Shneerson, 2001, Acta crystallographica. Section A, Foundations of crystallography.

The three-dimensional distribution of RNA and protein in the interior of tomato bushy stunt virus: a neutron low-resolution single-crystal diffraction study.

D. Wild, P. Timmins, J. Witz, 1994, Structure.

A Bayesian approach to reconstructing genetic regulatory networks with hidden factors

Zoubin Ghahramani, Matthew J. Beal, Francesco Falciani, 2005, Bioinform..

Discovering Temporal Patterns of Differential Gene Expression in Microarray Time Series

Zoubin Ghahramani, Andrew Meade, Oliver Stegle, 2009, GCB.

How to infer gene networks from expression profiles, revisited

Christopher A. Penfold, Christopher A Penfold, David L Wild, 2011, Interface Focus.

Protein analyst - a distributed object environment for protein sequence and structure analysis

David L. Wild, Mansoor A. S. Saqi, M. J. Hartshorn, 1999, Bioinform..

Time-series transcriptomics reveals that AGAMOUS-LIKE 22 affects 3 primary metabolism and developmental processes in drought-stressed 4 Arabidopsis 5 6 Running title : Temporal drought dynamics in Arabidopsis 7 8

Christopher A. Penfold, Dafyd J. Jenkins, T. Lawson, 2016 .

A graphical model for protein secondary structure prediction

Wei Chu, Zoubin Ghahramani, David L. Wild, 2004, ICML.

Protein secondary structure prediction using sigmoid belief networks to parameterize segmental semi-Markov models

Wei Chu, Zoubin Ghahramani, David L. Wild, 2004, ESANN.

Crystallographic analysis at low resolution of metabolite binding sites on phosphorylase b

L. Johnson, D. Wild, I. Weber, 1978 .

Accelerating Bayesian Hierarchical Clustering of Time Series Data with a Randomised Algorithm

Zoubin Ghahramani, David L. Wild, Richard S. Savage, 2013, PloS one.

Improving protein-protein interaction prediction using evolutionary information from low-quality MSAs

David L. Wild, Csilla Várnai, Nikolas S. Burkoff, 2017, PloS one.

Comment on "Efficient Monte Carlo trial moves for polypeptide simulations" [J. Chem. Phys. 123, 174905 (2005)].

A. Podtelezhnikov, D. Wild, 2008, The Journal of chemical physics.

CSI: a nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data

Christopher A. Penfold, Paul E. Brown, Christopher A Penfold, 2015, Statistical applications in genetics and molecular biology.

A Bayesian approach to reconstructing genetic regulatory networks with hidden factors

Matthew J. Beal, D. Wild, F. Falciani, 2005, Bioinform..

A Bayesian approach to reconstructing genetic regulatory networks with hidden factors

Matthew J. Beal, D. Wild, F. Falciani, 2005 .

Reconstructing Transcriptional Networks Using Gene Expression Profiling and Bayesian State-Space Models

Juan Li, Zoubin Ghahramani, Matthew J. Beal, 2007 .

Modeling genetic regulatory networks using gene expression profiling and state space models

Zoubin Ghahramani, David L. Wild, John E. Angus, 2005 .

Transcriptional Dynamics Driving MAMP-Triggered Immunity and Pathogen Effector-Mediated Immunosuppression in Arabidopsis Leaves Following Infection with Pseudomonas syringae pv tomato DC3000[OPEN]

Christopher A. Penfold, Dafyd J. Jenkins, George R. Littlejohn, 2015, Plant Cell.

Efficient Parameter Estimation of Generalizable Coarse-Grained Protein Force Fields Using Contrastive Divergence: A Maximum Likelihood Approach

David L. Wild, Csilla Várnai, Nikolas S. Burkoff, 2013, Journal of chemical theory and computation.

Bayesian hierarchical clustering for microarray time series data with replicates and outlier measurements

Paul D. W. Kirk, David L. Wild, Emma J. Cooke, 2011, BMC Bioinformatics.

Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks

Christopher A. Penfold, David L. Wild, Katherine J. Denby, 2012, Bioinform..

High-Resolution Temporal Profiling of Transcripts during Arabidopsis Leaf Senescence Reveals a Distinct Chronology of Processes and Regulation[C][W][OA]

Christopher A. Penfold, Dafyd J. Jenkins, D. Wild, 2011, Plant Cell.

THE CRYSTAL STRUCTURE OF GLYCOGEN PHOSPHORYLASE b

L. Johnson, D. Wild, I. Weber, 1978 .

Predicting protein β-sheet contacts using a maximum entropy-based correlated mutation measure

David L. Wild, Csilla Várnai, Nikolas S. Burkoff, 2013, Bioinform..

ParticleMDI: particle Monte Carlo methods for the cluster analysis of multiple datasets with applications to cancer subtype identification

Jim E. Griffin, David L. Wild, Nathan Cunningham, 2020, Adv. Data Anal. Classif..

particleMDI: A Julia Package for the Integrative Cluster Analysis of Multiple Datasets

Jim E. Griffin, Anthony Lee, David L. Wild, 2018, Springer Proceedings in Mathematics & Statistics.

CRANKITE: A fast polypeptide backbone conformation sampler

A. Podtelezhnikov, D. Wild, 2008, Source Code for Biology and Medicine.