G. Sanguinetti

发表

Missing Data in Kernel PCA

Neil D. Lawrence, Guido Sanguinetti, Neil D. Lawrence, 2006, ECML.

Matching Kernels through K ullback- L eibler Divergence Minimisation

Neil D. Lawrence, G. Sanguinetti, 2004 .

Bayesian Multitask Classification With Gaussian Process Priors

Guido Sanguinetti, Grigorios Skolidis, G. Sanguinetti, 2011, IEEE Transactions on Neural Networks.

Learning and Designing Stochastic Processes from Logical Constraints

Guido Sanguinetti, Luca Bortolussi, G. Sanguinetti, 2013, QEST.

Single-trial classification of EEG in a visual object task using ICA and machine learning

Guido Sanguinetti, Antje Nuthmann, Andrew X. Stewart, 2014, Journal of Neuroscience Methods.

Learning Temporal Logical Properties Discriminating ECG models of Cardiac Arrhytmias pdf

Ezio Bartocci, Guido Sanguinetti, Luca Bortolussi, 2013, ArXiv.

Advances in Neural Information Processing Systems 20 (NIPS 2007)

Botond Cseke, Manfred Opper, Guido Sanguinetti, 2008 .

Identifying Submodules of Cellular Regulatory Networks

Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti, 2006, CMSB.

CoSBi 13 / 2008 Calibration of biochemical network models

G. Sanguinetti, C. Priami, P. Lecca, 2009 .

Detecting repeated cancer evolution from multi-region tumor sequencing data

Giulio Caravagna, Guido Sanguinetti, Daniele Ramazzotti, 2018, Nature Methods.

Explorer Modelling transcriptional regulation using Gaussian processes

Neil D. Lawrence, G. Sanguinetti, 2006 .

Efficient Low-Order Approximation of First-Passage Time Distributions.

Guido Sanguinetti, Botond Cseke, Ramon Grima, 2017, Physical review letters.

BRIE: transcriptome-wide splicing quantification in single cells

G. Sanguinetti, Yuanhua Huang, 2017, Genome Biology.

scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells

G. Sanguinetti, J. Marioni, O. Stegle, 2018, Nature Communications.

Towards a systems level understanding of the oxygen response of Escherichia coli.

G. Sanguinetti, K. Hellingwerf, K. Bettenbrock, 2014, Advances in microbial physiology.

Machine Learning Methods in Statistical Model Checking and System Design – A Tutorial Introduction ?

G. Sanguinetti, L. Bortolussi, D. Milios, 2015 .

Cox process representation and inference for stochastic reaction–diffusion processes

G. Sanguinetti, R. Grima, David Schnoerr, 2016, Nature Communications.

Gene Regulatory Network Inference: An Introductory Survey.

G. Sanguinetti, V. A. Huynh-Thu, 2018, Methods in molecular biology.

Reprogramming of Escherichia coli K-12 Metabolism during the Initial Phase of Transition from an Anaerobic to a Micro-Aerobic Environment

G. Sanguinetti, A. Hounslow, M. Williamson, 2011, PloS one.

Explorer Transcription factor binding predicts histone modifications in human cell lines

Guido Sanguinetti, G. Sanguinetti, D. Sproul, 2017 .

Evolutionary dynamics of residual disease in human glioblastoma

George D. Cresswell, G. Sanguinetti, R. Verhaak, 2018, Annals of oncology : official journal of the European Society for Medical Oncology.

Network Performance Analysis

Guido Sanguinetti, G. Sanguinetti, 2022 .

ingle-trial classification of EEG in a visual object task using ICA and achine learning

G. Sanguinetti, ndrew X. Stewarta, Antje Nuthmannb, 2014 .

Automated extraction of meaningful pathways from quantitative proteomics data.

Guido Sanguinetti, Alfonso Jaramillo, Josselin Noirel, 2008, Briefings in functional genomics & proteomics.

Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments

Guido Sanguinetti, Alina Selega, Christel Sirocchi, 2016, Nature Methods.

Modelling transcriptional regulation using Gaussian Processes

Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti, 2006, NIPS.

Carbon Monoxide Gas Is Not Inert, but Global, in Its Consequences for Bacterial Gene Expression, Iron Acquisition, and Antibiotic Resistance

G. Sanguinetti, K. Hellingwerf, R. Poole, 2016, Antioxidants & redox signaling.

2 Stochastic ON / OFF models of gene expression

G. Sanguinetti, Andrea Ocone, 2013 .

Melissa: Bayesian clustering and imputation of single-cell methylomes

Guido Sanguinetti, Chantriolnt-Andreas Kapourani, G. Sanguinetti, 2019, Genome Biology.

Expectation propagation for continuous time stochastic processes

Guido Sanguinetti, Manfred Opper, Botond Cseke, 2015, 1512.06098.

Quantitative analysis questions the role of MeCP2 as a global regulator of alternative splicing

A. Bird, G. Sanguinetti, Kashyap Chhatbar, 2020, bioRxiv.

Dimensional Reduction of Clustered Data Sets

G. Sanguinetti, 2022 .

Factored expectation propagation for input-output FHMM models in systems biology

G. Sanguinetti, Botond Cseke, 2013, 1305.4153.

Detecting repeated cancer evolution in human tumours from multi-region sequencing data

G. Sanguinetti, T. Graham, A. Sottoriva, 2017, bioRxiv.

RiboAbacus: a model trained on polyribosome images predicts ribosome density and translational efficiency from mammalian transcriptomes

G. Sanguinetti, L. Montanaro, A. Pugliese, 2015, Nucleic acids research.

Transcriptome-wide splicing quantification in single cells

G. Sanguinetti, Yuanhua Huang, 2017, bioRxiv.

MLSB 14 The eighth International Workshop on Machine Learning in Systems Biology 6-7 September 2014

G. Sanguinetti, 2014 .

De novo prediction of RNA-protein interactions with Graph Neural Networks

G. Sanguinetti, V. Arora, 2021, bioRxiv.

Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks

G. Sanguinetti, L. Bortolussi, Ginevra Carbone, 2021, ArXiv.

Explorer Probabilistic Programming Process Algebra

G. Sanguinetti, J. Hillston, D. Milios, 2016 .

CO-Releasing Molecules Have Nonheme Targets in Bacteria: Transcriptomic, Mathematical Modeling and Biochemical Analyses of CORM-3 [Ru(CO)3Cl(glycinate)] Actions on a Heme-Deficient Mutant of Escherichia coli

G. Sanguinetti, R. Poole, B. Mann, 2015, Antioxidants & redox signaling.

Challenges for machine learning in RNA-protein interaction prediction

G. Sanguinetti, V. Arora, 2022, Statistical applications in genetics and molecular biology.

Point process modelling of the Afghan War Diary

Visakan Kadirkamanathan, Guido Sanguinetti, Andrew Zammit-Mangion, 2012, Proceedings of the National Academy of Sciences.

A variational approach for the online dual estimation of spatiotemporal systems governed by the IDE

G. Sanguinetti, V. Kadirkamanathan, A. Mangion, 2011 .

Peroxynitrite Toxicity in Escherichia coli K12 Elicits Expression of Oxidative Stress Responses and Protein Nitration and Nitrosylation*

G. Sanguinetti, R. Read, R. Poole, 2010, The Journal of Biological Chemistry.

M3D: a kernel-based test for shape changes in methylation profiles

Tom R. Mayo, G. Schweikert, G. Sanguinetti, 2014, 1410.6677.

HuD is a neural enhancer of global translation acting on mTORC1-responsive genes and sponged by the Y3 small non-coding RNA

G. Sanguinetti, D. Tollervey, P. Zuccotti, 2017, bioRxiv.

Bayesian Learning of Effective Chemical Master Equations in Crowded Intracellular Conditions

G. Sanguinetti, R. Grima, S. Braichenko, 2022, CMSB.

Adaptation of anaerobic cultures of E scherichia coli K‐12 in response to environmental trimethylamine‐N‐oxide

G. Sanguinetti, K. Denby, R. Poole, 2015, Environmental microbiology.

On the Robustness of Bayesian Neural Networks to Adversarial Attacks

G. Sanguinetti, L. Bortolussi, L. Laurenti, 2022, ArXiv.

Systems biology meets synthetic biology: a case study of the metabolic effects of synthetic rewiring.

Guido Sanguinetti, Josselin Noirel, Saw Yen Ow, 2009, Molecular bioSystems.

Edinburgh Research Explorer HuD is a neural translation enhancer acting on mTORC1-responsive genes and counteracted by the Y3 small non-coding RNA

G. Sanguinetti, D. Tollervey, P. Zuccotti, 2018 .

Supplementary material from "Geometric fluid approximation for general continuous-time Markov chains"

G. Sanguinetti, J. Hillston, M. Michaelides, 2019 .

Edinburgh Research Explorer Analysis of the Bacterial Response to Ru(CO) 3 Cl(Glycinate) (CORM-3) and the Inactivated Compound Identifies the Role Played by the Ruthenium Compound and Reveals Sulfur-Containing Species as a Major Target of CORM-3 Action .

G. Sanguinetti, R. Poole, B. Mann, 2017 .

Transcript Profiling and Inference of Escherichia coli K-12 ArcA Activity across the Range of Physiologically Relevant Oxygen Concentrations*

Alison I. Graham, G. Sanguinetti, A. T. Beek, 2011, Journal of Biological Chemistry.

Model reduction for the Chemical Master Equation: An information-theoretic approach.

G. Sanguinetti, R. Grima, Kaan Öcal, 2022, The Journal of chemical physics.

Explorer BRIE : transcriptome-wide splicing quantication in single cells

G. Sanguinetti, Yuanhua Huang, 2017 .

Categorial Mirror Symmetry for K3 Surfaces

G. Sanguinetti, U. Bruzzo, C. Bartocci, 1998, math-ph/9811004.

De novo prediction of RNA–protein interactions with graph neural networks

G. Sanguinetti, V. Arora, 2021, bioRxiv.

Deciphering the interaction of genetic and epigenetic factors through enhanced polymer models∗

G. Sanguinetti, A. Rosa, Alexander Zhang, 2022 .

SCRaPL: A Bayesian hierarchical framework for detecting technical associates in single cell multiomics data

G. Sanguinetti, C. Vallejos, Christos Maniatis, 2022, PLoS Comput. Biol..

Analysis of transcript changes in a heme-deficient mutant of Escherichia coli in response to CORM-3 [Ru(CO)3Cl(glycinate)]

G. Sanguinetti, R. Poole, Ronald Begg, 2015, Genomics data.

Systems analysis of transcription factor activities in environments with stable and dynamic oxygen concentrations

G. Sanguinetti, R. Poole, Jeffrey Green, 2012, Open Biology.

CydDC-mediated reductant export in Escherichia coli controls the transcriptional wiring of energy metabolism and combats nitrosative stress

G. Sanguinetti, M. Howard, M. Shepherd, 2015, The Biochemical journal.

The Broad-Spectrum Antimicrobial Potential of [Mn(CO)4(S2CNMe(CH2CO2H))], a Water-Soluble CO-Releasing Molecule (CORM-401): Intracellular Accumulation, Transcriptomic and Statistical Analyses, and Membrane Polarization

G. Sanguinetti, R. Poole, B. Mann, 2017, Antioxidants & redox signaling.

Advances in Neural Information Processing Systems 24

Guido Sanguinetti, Botond Cseke, Manfred Opper, 2011 .

Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics

Visakan Kadirkamanathan, Guido Sanguinetti, Mark Girolami, 2009 .

Dynamics of a starvation-to-surfeit shift: a transcriptomic and modelling analysis of the bacterial response to zinc reveals transient behaviour of the Fur and SoxS regulators.

Guido Sanguinetti, Alison I. Graham, G. Sanguinetti, 2012, Microbiology.

Sparse Approximate Inference for Spatio-Temporal Point Process Models

Botond Cseke, Andrew Zammit-Mangion, Tom Heskes, 2013, 1305.4152.

Systematic errors in estimates of $R_t$ from symptomatic cases in the presence of observation bias

G. Sanguinetti, 2020, 2012.02105.

Transcription rate strongly affects splicing fidelity and cotranscriptionality in budding yeast

G. Sanguinetti, J. Beggs, Yuanhua Huang, 2018, Genome research.

Gene Regulatory Networks

Guido Sanguinetti, Van Anh Huynh-Thu, G. Sanguinetti, 2019, Methods in Molecular Biology.

Single cell multi-omics profiling reveals a hierarchical epigenetic landscape during mammalian germ layer specification

G. Sanguinetti, J. Marioni, F. Buettner, 2019, bioRxiv.

Multi-omics profiling of mouse gastrulation at single cell resolution

G. Sanguinetti, J. Marioni, B. Göttgens, 2019, Nature.

Kinetic CRAC uncovers a role for Nab3 in determining gene expression profiles during stress

Guido Sanguinetti, Gabriele Schweikert, Alina Selega, 2017, Nature Communications.

Multi-omics profiling of mouse gastrulation at single cell resolution

G. Sanguinetti, J. Marioni, B. Göttgens, 2019, Nature.

Network of epistatic interactions within a yeast snoRNA

Guido Sanguinetti, Botond Cseke, David Tollervey, 2016, Science.

On learning the structure of Bayesian Networks and submodular function maximization pdf

Giulio Caravagna, Guido Sanguinetti, Daniele Ramazzotti, 2017, ArXiv.

scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution

G. Sanguinetti, C. Vallejos, Chantriolnt-Andreas Kapourani, 2021, Genome Biology.

scMET: Bayesian modelling of DNA methylation heterogeneity at single-cell resolution

Catalina A. Vallejos, Ricard Argelaguet, Guido Sanguinetti, 2020, bioRxiv.

Subclonal reconstruction of tumors using machine learning and population genetics

Giulio Caravagna, Guido Sanguinetti, Benjamin Werner, 2020, Nature Genetics.

Final version 1 Model-based tumor subclonal 1 reconstruction 2 3

Marc J. Williams, George D. Cresswell, G. Sanguinetti, 2020 .

Geometric fluid approximation for general continuous-time Markov chains

Guido Sanguinetti, Jane Hillston, Michalis Michaelides, 2019, Proceedings of the Royal Society A.

MeCP2 recognizes cytosine methylated tri-nucleotide and di-nucleotide sequences to tune transcription in the mammalian brain

A. Bird, G. Schweikert, G. Sanguinetti, 2017, PLoS genetics.

Domains of methylated CAC and CG target MeCP2 to tune transcription in the brain

A. Bird, G. Schweikert, G. Sanguinetti, 2016, bioRxiv.

Statistical modeling of isoform splicing dynamics from RNA-seq time series data

Guido Sanguinetti, Yuanhua Huang, G. Sanguinetti, 2016, Bioinform..

Mixture Model on Graphs: A Probabilistic Model for Network-Based Analysis of Proteomic Data

Guido Sanguinetti, Phillip C. Wright, Josselin Noirel, 2010 .

Using BRIE to Detect and Analyze Splicing Isoforms in scRNA-Seq Data.

Guido Sanguinetti, Yuanhua Huang, G. Sanguinetti, 2019, Methods in molecular biology.

Transcriptome-wide RNA processing kinetics revealed using extremely short 4tU labeling

G. Sanguinetti, J. Beggs, Yuanhua Huang, 2015, Genome Biology.

Strand-specific , high-resolution mapping of modified RNA polymerase II

G. Sanguinetti, E. Petfalski, D. Tollervey, 2016 .

Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3.

Vân Anh Huynh-Thu, Guido Sanguinetti, G. Sanguinetti, 2018, Methods in molecular biology.

Combining tree-based and dynamical systems for the inference of gene regulatory networks

Guido Sanguinetti, Vân Anh Huynh-Thu, G. Sanguinetti, 2015, Bioinform..

ABC-Fun: A Probabilistic Programming Language for Biology

Guido Sanguinetti, Jane Hillston, Anastasis Georgoulas, 2013, CMSB.

A stochastic hybrid model of a biological filter

Guido Sanguinetti, Andrea Ocone, G. Sanguinetti, 2013, HAS.

Hybrid regulatory models: a statistically tractable approach to model regulatory network dynamics

Guido Sanguinetti, Andrew J. Millar, Andrea Ocone, 2013, Bioinform..

Learning combinatorial transcriptional dynamics from gene expression data

Guido Sanguinetti, Manfred Opper, M. Opper, 2010, Bioinform..

Computational Methods in Systems Biology: 17th International Conference, CMSB 2019, Trieste, Italy, September 18–20, 2019, Proceedings

Luca Bortolussi, Guido Sanguinetti, G. Sanguinetti, 2019, CMSB.

Model-based tumor subclonal reconstruction

Giulio Caravagna, Guido Sanguinetti, Marc J. Williams, 2019, bioRxiv.

HuD Is a Neural Translation Enhancer Acting on mTORC1-Responsive Genes and Counteracted by the Y3 Small Non-coding RNA

G. Sanguinetti, D. Tollervey, P. Zuccotti, 2018, Molecular cell.

LEAFPROCESSOR: a new leaf phenotyping tool using contour bending energy and shape cluster analysis.

Guido Sanguinetti, Andreas Backhaus, Nick Monk, 2010, The New phytologist.

Carbon Monoxide-releasing Antibacterial Molecules Target Respiration and Global Transcriptional Regulators*

Guido Sanguinetti, G. Sanguinetti, R. Poole, 2009, Journal of Biological Chemistry.

Inference and uncertainty quantification of stochastic gene expression via synthetic models

G. Sanguinetti, R. Grima, Kaan Öcal, 2022, bioRxiv.

Approximation and inference methods for stochastic biochemical kinetics—a tutorial review

Ramon Grima, David Schnoerr, Guido Sanguinetti, 2016, 1608.06582.

The complex chemical Langevin equation.

Guido Sanguinetti, Ramon Grima, David Schnoerr, 2014, The Journal of chemical physics.

Comparison of different moment-closure approximations for stochastic chemical kinetics.

Guido Sanguinetti, Ramon Grima, David Schnoerr, 2015, The Journal of chemical physics.

Quantitative analysis questions the role of MeCP2 as a global regulator of alternative splicing

A. Bird, G. Sanguinetti, Kashyap Chhatbar, 2020, bioRxiv.

Reconstructing transcription factor activities in hierarchical transcription network motifs

Guido Sanguinetti, Andrea Ocone, G. Sanguinetti, 2011, Bioinform..

BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments

G. Sanguinetti, Yuanhua Huang, 2021, Genome biology.

BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments

G. Sanguinetti, Yuanhua Huang, 2020, Genome Biology.

M3D: a kernel-based test for spatially correlated changes in methylation profiles

Guido Sanguinetti, Tom R. Mayo, Gabriele Beate Schweikert, 2014, Bioinform..

diffBUM-HMM: a robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing data

Ka Ying Toby Law, G. Sanguinetti, S. Granneman, 2021, Genome Biology.

Differential BUM-HMM: a robust statistical modelling approach for detecting RNA flexibility changes in high-throughput structure probing data

Guido Sanguinetti, Paolo Marangio, Sander Granneman, 2020, bioRxiv.

The MOBSTER R package for tumour subclonal deconvolution from bulk DNA whole-genome sequencing data

Giulio Caravagna, Guido Sanguinetti, Trevor A. Graham, 2020, BMC Bioinform..

Compensations for Diminished Terminal Oxidase Activity in Escherichia coli

Guido Sanguinetti, Mark Shepherd, G. Sanguinetti, 2010, The Journal of Biological Chemistry.

Higher order methylation features for clustering and prediction in epigenomic studies

Guido Sanguinetti, Chantriolnt-Andreas Kapourani, G. Sanguinetti, 2016, Bioinform..

PartCrafter: find, generate and analyze BioParts

Shay B. Cohen, Guido Sanguinetti, Emily Scher, 2019, Synthetic biology.

Neural field models for latent state inference: Application to large-scale neuronal recordings

Guido Sanguinetti, Matthias H. Hennig, Michael E Rule, 2019, bioRxiv.

Autoregressive Point Processes as Latent State-Space Models: A Moment-Closure Approach to Fluctuations and Autocorrelations

Guido Sanguinetti, Michael Rule, G. Sanguinetti, 2018, Neural Computation.

A Perturbative Approach to Novelty Detection in Autoregressive Models

Guido Sanguinetti, Maurizio Filippone, G. Sanguinetti, 2011, IEEE Transactions on Signal Processing.

Information theoretic novelty detection

Guido Sanguinetti, Maurizio Filippone, G. Sanguinetti, 2010, Pattern Recognit..

Random Projections for Improved Adversarial Robustness

Guido Sanguinetti, Luca Bortolussi, Ginevra Carbone, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

SCRaPL: hierarchical Bayesian modelling of associations in single cell multi-omics data

G. Sanguinetti, C. Vallejos, Christos Maniatis, 2021, bioRxiv.

Statistical Abstraction for Multi-scale Spatio-temporal Systems

Guido Sanguinetti, Jane Hillston, Michalis Michaelides, 2017, QEST.

ProPPA: Probabilistic Programming for Stochastic Dynamical Systems

Guido Sanguinetti, Jane Hillston, Anastasis Georgoulas, 2018, ACM Trans. Model. Comput. Simul..

Probabilistic Programming Process Algebra

Guido Sanguinetti, Jane Hillston, Dimitrios Milios, 2014, QEST.

Multi-task learning for pKa prediction

Guido Sanguinetti, Matthias Rupp, Grigorios Skolidis, 2012, Journal of Computer-Aided Molecular Design.

A Bayesian approach for structure learning in oscillating regulatory networks

Guido Sanguinetti, Andrew J. Millar, Daniel Trejo-Baños, 2015, Bioinform..

Temporal Logic Based Monitoring of Assisted Ventilation in Intensive Care Patients

Ezio Bartocci, Guido Sanguinetti, Luca Bortolussi, 2014, ISoLA.

Robustness of Bayesian Neural Networks to Gradient-Based Attacks

G. Sanguinetti, L. Bortolussi, L. Laurenti, 2020, NeurIPS.

Dimensionality Reduction of Clustered Data Sets

Guido Sanguinetti, G. Sanguinetti, 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Novelty detection in autoregressive models using information theoretic measures

G. Sanguinetti, M. Filippone, 2009 .

Attacks on Online Learners: a Teacher-Student Analysis

G. Sanguinetti, Sebastian Goldt, R. Margiotta, 2023, arXiv.org.

Conflict Data Sets and Point Patterns

Visakan Kadirkamanathan, Michael Dewar, Guido Sanguinetti, 2013 .

Learning and Inference in Computational Systems Biology

Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti, 2010, Computational molecular biology.

puma: a Bioconductor package for propagating uncertainty in microarray analysis

Neil D. Lawrence, Magnus Rattray, Xuejun Liu, 2009, BMC Bioinformatics.

MMDiff: quantitative testing for shape changes in ChIP-Seq data sets

Guido Sanguinetti, Botond Cseke, Adrian Bird, 2013, BMC Genomics.

Policy learning in continuous-time Markov decision processes using Gaussian Processes

Ezio Bartocci, Guido Sanguinetti, Tomás Brázdil, 2017, Perform. Evaluation.

Policy Learning for Time-Bounded Reachability in Continuous-Time Markov Decision Processes via Doubly-Stochastic Gradient Ascent

Ezio Bartocci, Guido Sanguinetti, Tomás Brázdil, 2016, QEST.

Transition of Escherichia coli from Aerobic to Micro-aerobic Conditions Involves Fast and Slow Reacting Regulatory Components*

Guido Sanguinetti, G. Sanguinetti, R. Poole, 2007, Journal of Biological Chemistry.

Unbiased Bayesian inference for population Markov jump processes via random truncations

Guido Sanguinetti, Jane Hillston, Anastasis Georgoulas, 2015, Stat. Comput..

On the Robustness of Temporal Properties for Stochastic Models

Ezio Bartocci, Guido Sanguinetti, Luca Bortolussi, 2013, HSB.

Melissa: Bayesian clustering and imputation of single-cell methylomes

Guido Sanguinetti, Chantriolnt Andreas Kapourani, G. Sanguinetti, 2018, Genome Biology.

Analysis of the bacterial response to Ru(CO)3Cl(Glycinate) (CORM-3) and the inactivated compound identifies the role played by the ruthenium compound and reveals sulfur-containing species as a major target of CORM-3 action.

Guido Sanguinetti, G. Sanguinetti, R. Poole, 2013, Antioxidants & redox signaling.

Quantifying lottery tickets under label noise: accuracy, calibration, and complexity

G. Sanguinetti, Sebastian Goldt, V. Arora, 2023, UAI.

Propagating uncertainty in microarray data analysis

Neil D. Lawrence, Magnus Rattray, Xuejun Liu, 2006, Briefings Bioinform..

Variational Estimation in Spatiotemporal Systems From Continuous and Point-Process Observations

Visakan Kadirkamanathan, Guido Sanguinetti, Andrew Zammit Mangion, 2012, IEEE Transactions on Signal Processing.

Advances in Neural Information Processing Systems 26 (NIPS 2013)

M. Opper, G. Sanguinetti, Botond Cseke, 2013 .

Variational inference for Markov jump processes

Guido Sanguinetti, Manfred Opper, M. Opper, 2007, NIPS.

System design of stochastic models using robustness of temporal properties

Ezio Bartocci, Guido Sanguinetti, Luca Bortolussi, 2015, Theor. Comput. Sci..

Uncertainty versus variability: Bayesian methods for analysis of scRNA-seq data

Guido Sanguinetti, Yuanhua Huang, G. Sanguinetti, 2021 .

Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities

Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti, 2006, Bioinform..

A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription

Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti, 2006, Bioinform..

MMDiff: quantitative testing for shape changes in ChIP-Seq data sets

A. Bird, G. Schweikert, G. Sanguinetti, 2013, BMC Genomics.

Calibration of biochemical network models

Paola Lecca, Guido Sanguinetti, Corrado Priami, 2008 .

Validity conditions for moment closure approximations in stochastic chemical kinetics.

Guido Sanguinetti, Ramon Grima, David Schnoerr, 2014, The Journal of chemical physics.

Wasserstein Distances for Estimating Parameters in Stochastic Reaction Networks

Guido Sanguinetti, Ramon Grima, Kaan Öcal, 2019, CMSB.

Parameter estimation for biochemical reaction networks using Wasserstein distances

Guido Sanguinetti, Ramon Grima, Kaan Ocal, 2019, Journal of Physics A: Mathematical and Theoretical.

A Shift toward Smaller Cell Size via Manipulation of Cell Cycle Gene Expression Acts to Smoothen Arabidopsis Leaf Shape1[W]

G. Sanguinetti, A. Fleming, Andreas Backhaus, 2011, Plant Physiology.

BPRMeth: a flexible Bioconductor package for modelling methylation profiles

Guido Sanguinetti, Chantriolnt-Andreas Kapourani, G. Sanguinetti, 2018, Bioinform..

Strand-specific , high-resolution mapping of modified RNA polymerase II

G. Sanguinetti, E. Petfalski, D. Tollervey, 2016 .

Transcriptome-wide RNA processing kinetics revealed using extremely short 4tU labeling

G. Sanguinetti, J. Beggs, Yuanhua Huang, 2015, Genome Biology.

A Statistical Approach for Computing Reachability of Non-linear and Stochastic Dynamical Systems

Guido Sanguinetti, Luca Bortolussi, G. Sanguinetti, 2014, QEST.

U-Check: Model Checking and Parameter Synthesis Under Uncertainty

Guido Sanguinetti, Luca Bortolussi, Dimitrios Milios, 2015, QEST.

Smoothed model checking for uncertain Continuous-Time Markov Chains

Luca Bortolussi, Dimitrios Milios, Guido Sanguinetti, 2014, Inf. Comput..

Learning and Designing Stochastic Processes from Logical Constraints

G. Sanguinetti, M. Stoelinga, L. Bortolussi, 2013, QEST.

Progeroid syndromes: models for stem cell aging?

G. Sanguinetti, W. Keith, I. Bellantuono, 2011, Biogerontology.

Approximate inference in latent Gaussian-Markov models from continuous time observations

Guido Sanguinetti, Manfred Opper, Botond Cseke, 2013, NIPS.

Online Variational Inference for State-Space Models with Point-Process Observations

Visakan Kadirkamanathan, Guido Sanguinetti, Mahesan Niranjan, 2011, Neural Computation.

Dimensionality Reduction of Clustered Data Sets

G. Sanguinetti, 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Accounting for probe-level noise in principal component analysis of microarray data

Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti, 2005, Bioinform..

Carbon Monoxide Gas Is Not Inert, but Global, in Its Consequences for Bacterial Gene Expression, Iron Acquisition, and Antibiotic Resistance

G. Sanguinetti, K. Hellingwerf, R. Poole, 2016, Antioxidants & redox signaling.

TFInfer: a tool for probabilistic inference of transcription factor activities

Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti, 2010, Bioinform..

Identifying differentially expressed subnetworks with MMG

Guido Sanguinetti, Phillip C. Wright, Josselin Noirel, 2008, Bioinform..

MMG: a probabilistic tool to identify submodules of metabolic pathways

Guido Sanguinetti, Phillip C. Wright, Josselin Noirel, 2008, Bioinform..

A Case Study on Meta-Generalising: A Gaussian Processes Approach

Guido Sanguinetti, Grigorios Skolidis, G. Sanguinetti, 2012, J. Mach. Learn. Res..

Intrinsic Geometric Vulnerability of High-Dimensional Artificial Intelligence pdf

Guido Sanguinetti, Luca Bortolussi, G. Sanguinetti, 2018, ArXiv.

DGW: an exploratory data analysis tool for clustering and visualisation of epigenomic marks

Guido Sanguinetti, Roberto Visintainer, Gabriele Beate Schweikert, 2013, BMC Bioinformatics.

Approximate inference of the bandwidth in multivariate kernel density estimation

Guido Sanguinetti, Maurizio Filippone, G. Sanguinetti, 2011, Comput. Stat. Data Anal..

Attacks on Online Learners: a Teacher-Student Analysis

G. Sanguinetti, Sebastian Goldt, R. Margiotta, 2023, ArXiv.

Bayesian clustering and imputation of single cell methylomes

Guido Sanguinetti, Andreas C. Kapourani, G. Sanguinetti, 2018 .

Semisupervised Multitask Learning With Gaussian Processes

Guido Sanguinetti, Grigorios Skolidis, G. Sanguinetti, 2013, IEEE Transactions on Neural Networks and Learning Systems.

Modeling Conflict Dynamics with Spatio-temporal Data

Visakan Kadirkamanathan, Guido Sanguinetti, Michael Dewar, 2013, Springer Briefs in Applied Sciences and Technology.

SHARE-Topic: Bayesian interpretable modeling of single-cell multi-omic data

G. Sanguinetti, Nour El Kazwini, 2023, bioRxiv.