M. Grzegorczyk

发表

Regularization of non-homogeneous dynamic Bayesian networks with global information-coupling based on hierarchical Bayesian models

Marco Grzegorczyk, Dirk Husmeier, M. Grzegorczyk, 2013, Machine Learning.

Statistical inference of regulatory networks for circadian regulation

M. Grzegorczyk, D. Husmeier, A. Aderhold, 2014, Statistical applications in genetics and molecular biology.

Bayesian network based procedure for regional drought monitoring: The Seasonally Combinative Regional Drought Indicator.

A. Shoukry, S. Gani, M. Grzegorczyk, 2020, Journal of environmental management.

ASSESSMENT OF REGRESSION METHODS FOR INFERENCE OF REGULATORY NETWORKS INVOLVED IN CIRCADIAN REGULATION

V. Smith, M. Grzegorczyk, D. Husmeier, 2013 .

Inferring bi-directional interactions between circadian clock genes and metabolism with model ensembles

M. Grzegorczyk, D. Husmeier, A. Aderhold, 2015, Statistical applications in genetics and molecular biology.

Characterization of regional hydrological drought using improved precipitation records under multi-auxiliary information

M. Grzegorczyk, I. Hussain, I. Almanjahie, 2019, Theoretical and Applied Climatology.

A Probabilistic Weighted Joint Aggregative Drought Index (PWJADI) criterion for drought monitoring systems

Dost Muhammad Khan, M. Grzegorczyk, I. Hussain, 2019, Tellus A: Dynamic Meteorology and Oceanography.

Statistical Applications in Genetics and Molecular Biology A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology

Marco Grzegorczyk, Dirk Husmeier, M. Grzegorczyk, 2012 .

Advanced applications of Bayesian networks in systems biology

M. Grzegorczyk, D. Husmeier, A. Werhli, 2011 .

Overview and Evaluation of Recent Methods for Statistical Inference of Gene Regulatory Networks from Time Series Data.

Marco Grzegorczyk, Dirk Husmeier, Andrej Aderhold, 2018, Methods in molecular biology.

Improving nonhomogeneous dynamic Bayesian networks with sequentially coupled parameters

M. Grzegorczyk, Mahdi Shafiee Kamalabad, 2018 .

A Bayesian Network Analysis of the Diagnostic Process and its Accuracy to Determine How Clinicians Estimate Cardiac Function in Critically Ill Patients: Prospective Observational Cohort Study

M. Wiering, M. Grzegorczyk, I. V. D. van der Horst, 2019, JMIR medical informatics.

On the more generalized non‐parametric framework for the propagation of uncertainty in drought monitoring

A. Shoukry, S. Gani, M. Grzegorczyk, 2020, Meteorological Applications.

Measuring and restructuring the risk in forecasting drought classes: an application of weighted Markov chain based model for standardised precipitation evapotranspiration index (SPEI) at one-month time scale

M. Grzegorczyk, G. Ni, Sadia Qamar, 2020 .

Model averaging for sparse seemingly unrelated regression using Bayesian networks among the errors

M. Grzegorczyk, A. Salam, 2022, Computational Statistics.

The ‘un-shrunk’ partial correlation in Gaussian graphical models

M. Grzegorczyk, V. Guryev, R. Bischoff, 2020, BMC Bioinformatics.

Stochastic modelling of cell differentiation networks from partially-observed clonal tracking data

M. Grzegorczyk, D. Pellin, L. Del Core, 2022, bioRxiv.

Comparative evaluation of different graphical models for the analysis of gene expression data

Marco Grzegorczyk, M. Grzegorczyk, 2006 .

Extracting Protein Regulatory Networks with Graphical Models

Marco Grzegorczyk, M. Grzegorczyk, 2007, Proteomics.

Non-invasive detection of colorectal tumours by the combined application of molecular diagnosis and the faecal occult blood test.

M. Grzegorczyk, K. Müller, W. Urfer, 2005, Cancer letters.

Modeling dragonfly population data with a Bayesian bivariate geometric mixed-effects model

M. Grzegorczyk, W. Krijnen, C. Brochard, 2022, Journal of Applied Statistics.

Comparative evaluation of various frequentist and Bayesian non-homogeneous Poisson counting models

M. Grzegorczyk, Mahdi Shafiee Kamalabad, 2016, Computational Statistics.

Stochastic inference of clonal dominance in gene therapy studies

M. Grzegorczyk, E. Wit, L. D. Core, 2022, bioRxiv.

Regularization of non-homogeneous dynamic Bayesian networks with global information-coupling based on hierarchical Bayesian models

M. Grzegorczyk, D. Husmeier, 2013, Machine Learning.

Mahalanobis distance, a novel statistical proxy of homeostasis loss is longitudinally associated with risk of type 2 diabetes

M. Connelly, M. Grzegorczyk, G. Navis, 2021, EBioMedicine.

Partially non-homogeneous dynamic Bayesian networks based on Bayesian regression models with partitioned design matrices

Marco Grzegorczyk, Mahdi Shafiee Kamalabad, Alexander Martin Heberle, 2018, Bioinform..

A non-homogeneous dynamic Bayesian network with a hidden Markov model dependency structure among the temporal data points

Marco Grzegorczyk, M. Grzegorczyk, 2016, Machine Learning.

Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters

Marco Grzegorczyk, Dirk Husmeier, M. Grzegorczyk, 2012, AISTATS.

Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes

Marco Grzegorczyk, Dirk Husmeier, M. Grzegorczyk, 2011, Bioinform..

Non-homogeneous dynamic Bayesian networks for continuous data

Marco Grzegorczyk, Dirk Husmeier, M. Grzegorczyk, 2011, Machine Learning.

Modelling Nonstationary Gene Regulatory Processes

Marco Grzegorczyk, Dirk Husmeier, Jörg Rahnenführer, 2010, Adv. Bioinformatics.

Non-stationary continuous dynamic Bayesian networks

Marco Grzegorczyk, Dirk Husmeier, M. Grzegorczyk, 2009, NIPS.

Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks

Marco Grzegorczyk, Dirk Husmeier, Adriano Velasque Werhli, 2006, Bioinform..

An introduction to Gaussian Bayesian networks.

Marco Grzegorczyk, M. Grzegorczyk, 2010, Methods in molecular biology.

Comparative evaluation of various frequentist and Bayesian non-homogeneous Poisson counting models

Marco Grzegorczyk, Mahdi Shafiee Kamalabad, M. Grzegorczyk, 2017, Comput. Stat..

Comparison of Two Different Stochastic Models for Extracting Protein Regulatory Pathways Using Bayesian Networks

Marco Grzegorczyk, M. Grzegorczyk, 2008, Journal of toxicology and environmental health. Part A.

A new Bayesian piecewise linear regression model for dynamic network reconstruction

Marco Grzegorczyk, Mahdi Shafiee Kamalabad, M. Grzegorczyk, 2021, BMC Bioinformatics.

Non-homogeneous dynamic Bayesian networks with edge-wise sequentially coupled parameters

Marco Grzegorczyk, Mahdi Shafiee Kamalabad, M. Grzegorczyk, 2019, Bioinform..

On the Bayesian network based data mining framework for the choice of appropriate time scale for regional analysis of drought Hazard

M. Grzegorczyk, Sadia Qamar, A. Khalique, 2021, Theoretical and Applied Climatology.

Normalization of clonal diversity in gene therapy studies using shape constrained splines

M. Grzegorczyk, D. Cesana, E. Montini, 2022, Scientific Reports.

A mixed-effects stochastic model reveals clonal dominance in gene therapy safety studies

M. Grzegorczyk, D. Pellin, L. Del Core, 2023, BMC Bioinformatics.

Modelling non-stationary dynamic gene regulatory processes with the BGM model

Marco Grzegorczyk, Dirk Husmeier, Jörg Rahnenführer, 2011, Comput. Stat..

Modelling Non‐homogeneous Dynamic Bayesian Networks with Piecewise Linear Regression Models

Marco Grzegorczyk, Dirk Husmeier, M. Grzegorczyk, 2019, Handbook of Statistical Genomics.

Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move

Marco Grzegorczyk, Dirk Husmeier, M. Grzegorczyk, 2008, Machine Learning.

Absolute β-catenin concentrations in Wnt pathway-stimulated and non-stimulated cells

M. Grzegorczyk, O. Müller, C. Kuhnen, 2006, Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals.

Comparative evaluation of reverse engineering gene regulatory networks with relevance networks , graphical gaussian models and bayesian networks

M. Grzegorczyk, D. Husmeier, A. Werhli, 2006 .

Being Bayesian about learning Gaussian Bayesian networks from incomplete data

M. Grzegorczyk, 2023, Int. J. Approx. Reason..

Exact hypothesis testing for shrinkage-based Gaussian graphical models

Marco Grzegorczyk, Victor Guryev, Peter Horvatovich, 2019, Bioinform..

Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler

Marco Grzegorczyk, Dirk Husmeier, Andrew J. Millar, 2008, Bioinform..

Approximate Bayesian inference in semi-mechanistic models

Marco Grzegorczyk, Dirk Husmeier, Andrej Aderhold, 2016, Statistics and Computing.

Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration

Marco Grzegorczyk, Dirk Husmeier, Andrej Aderhold, 2017, Computational Statistics.

Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks

M. Grzegorczyk, D. Husmeier, A. Werhli, 2006, Bioinform..

Statistics for Proteomics: A Review of Tools for Analyzing Experimental Data

Marco Grzegorczyk, Wolfgang Urfer, Klaus Jung, 2006, Proteomics.

A new Bayesian multivariate exponentially weighted moving average control chart for phase II monitoring of multivariate multiple linear profiles

Marco Grzegorczyk, Amirhossein Amiri, Ali Zeinal Hamadani, 2019, Qual. Reliab. Eng. Int..

Nonparametric Bayesian Networks

Marco Grzegorczyk, Katja Ickstadt, Hernán E. Grecco, 2011 .

Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks

M. Grzegorczyk, D. Husmeier, A. Werhli, 2006, Bioinform..