P. Sykacek

发表

Learning interaction dynamics with coupled hidden Markov models

S. Roberts, I. Rezek, P. Sykacek, 2000 .

A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control consortium

David P. Kreil, Todd M. Smith, Thomas M. Blomquist, 2014, Nature Biotechnology.

On Input Selection with Reversible Jump Markov Chain Monte Carlo Sampling

Peter Sykacek, P. Sykacek, 1999, NIPS.

RNA Interference in Ageing Research – A Mini-Review

Peter Sykacek, David P. Kreil, Brian Godsey, 2010, Gerontology.

Model-based probe set optimization for high-performance microarrays

David P. Kreil, I. Hofacker, P. Sykacek, 2008, Nucleic acids research.

Gene expression Bayesian modelling of shared gene function

P. Sykacek, C. Print, G. Micklem, 2007 .

Brain Computer Interfacing : State of the Art , Probabilistic Advances and Future Perspectives

P. Sykacek, 2003 .

Detecting and correcting systematic variation in large-scale RNA sequencing data

Wei Shi, Leming Shi, Jean Thierry-Mieg, 2014, Nature Biotechnology.

Metalevel Learning -is More than Model Selection Necessary?

P. Sykacek, 2007 .

Systematic protein complex profiling and differential analysis from co-fractionation mass spectrometry data

P. Sykacek, R. Aebersold, Chen Li, 2020, bioRxiv.

Root traits of European Vicia faba cultivars-Using machine learning to explore adaptations to agroclimatic conditions.

Peter Sykacek, Gernot Bodner, Boris Rewald, 2017, Plant, cell & environment.

Identification of Activating Mutations in the Transmembrane and Extracellular Domains of EGFR

P. Sykacek, C. Obinger, C. Oostenbrink, 2022, Biochemistry.

Predictive Monitoring of Shake Flask Cultures with Online Estimated Growth Models

P. Sykacek, C. Herwig, G. John, 2021, Bioengineering.

Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation

Stephen J. Roberts, Peter Sykacek, Maria Stokes, 2004, IEEE Transactions on Biomedical Engineering.

Adaptive Classification by Variational Kalman Filtering

Stephen J. Roberts, Peter Sykacek, S. Roberts, 2002, NIPS.

The transcriptome of syncytia induced by the cyst nematode Heterodera schachtii in Arabidopsis roots

David P. Kreil, P. Sykacek, F. Grundler, 2008, The Plant journal : for cell and molecular biology.

A dual role for autophagy in a murine model of lung cancer

P. Sykacek, G. Kroemer, Karin Aumayr, 2014, Nature Communications.

The impact of quantitative optimization of hybridization conditions on gene expression analysis

Gos Micklem, Peter Sykacek, Steven Russell, 2011, BMC Bioinformatics.

Bayesian assignment of gene ontology terms to gene expression experiments

Peter Sykacek, P. Sykacek, 2012, Bioinform..

Bacterial protein signals are associated with Crohn’s disease

David P. Kreil, J. Gibrat, J. Doré, 2014, Gut.

PCprophet: a framework for protein complex prediction and differential analysis using proteomic data

P. Sykacek, R. Aebersold, A. Purcell, 2021, Nature Methods.

PhosphoFlowSeq - A high-throughput kinase activity assay for screening drug resistance mutations in EGFR.

P. Sykacek, C. Obinger, M. Lehner, 2021, Journal of molecular biology.

PCprophet: a framework for protein complex prediction and differential analysis using proteomic data

P. Sykacek, R. Aebersold, A. Purcell, 2021, Nature Methods.

Gene expression changes in long term expanded human neural progenitor cells passaged by chopping lead to loss of neurogenic potential in vivo

P. Sykacek, D. Menon, T. Foltynie, 2007, Experimental Neurology.

Detection of the EEG Artifacts by the Means of the (Extended) Kalman Filter

Georg Dorffner, Peter Sykacek, P. Sykacek, 2001 .

An analysis of single amino acid repeats as use case for application specific background models

Peter Sykacek, Pawel P. Labaj, David P. Kreil, 2011, BMC Bioinformatics.

Improving biosignal processing through modeling uncertainty: Bayes vs. non-bayes in sleep staging

Georg Dorffner, Peter Rappelsberger, Peter Sykacek, 2002, Appl. Artif. Intell..

Cognitive tasks for driving a brain-computer interfacing system: a pilot study

M. Stokes, P. Sykacek, E. Curran, 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Probabilistic methods in BCI research

M. Stokes, S. Roberts, M. Gibbs, 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Ensemble Coupled Hidden Markov Models for Joint Characterisation of Dynamic Signals

Stephen J. Roberts, Iead Rezek, Peter Sykacek, 2003, AISTATS.

Biological assessment of robust noise models in microarray data analysis

Peter Sykacek, K. Felsenstein, Alexandra Posekany, 2011, Bioinform..

Bayes Consistent Classification of EEG Data by Approximate Marginalization

Stephen J. Roberts, Iead Rezek, Peter Sykacek, 2005 .

An automatic, continuous and probabilistic sleep stager based on a hidden markov model

Georg Dorffner, Arthur Flexer, Peter Sykacek, 2002, Appl. Artif. Intell..

A Probabilistic Approach to High-Resolution Sleep Analysis

Stephen J. Roberts, Georg Dorffner, Arthur Flexer, 2001, ICANN.

Using hidden Markov models to build an automatic, continuous and probabilistic sleep stager

Georg Dorffner, Arthur Flexer, Iead Rezek, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Investigating Explanatory Factors of Machine Learning Models for Plant Classification

P. Sykacek, Wilfried Wöber, L. Mehnen, 2021, Plants.

Identifying geographically differentiated features of Ethopian Nile tilapia (Oreochromis niloticus) morphology with machine learning

P. Sykacek, E. Alemayehu, Wilfried Wöber, 2021, PloS one.

Bayesian Modelling of Shared Gene Function

Gos Micklem, Peter Sykacek, Cristin G. Print, 2022 .

Coupled hidden Markov models for biosignal interaction modelling

Stephen J. Roberts, Peter Sykacek, L. Rezek, 2000 .

Bayesian Modelling of Shared Gene Function

P. Sykacek, G. Micklem, R. Clarkson, 2022 .

Online Supplement to “ Bayesian Modeling of Shared Gene Function ”

P. Sykacek, C. Print, G. Micklem, 2007 .

RELEVANT INPUT FEATURES FOR SLEEP STAGING WITH NEURAL NETWORKS

Georg Dorffner, Josef Zeitlhofer, P. Sykacek, 1997 .

A friendly statistics package for microarray analysis

Gos Micklem, Peter Sykacek, R. A. Furlong, 2005, Bioinform..

Equivalent error bars for neural network classifiers trained by Bayesian inference

Peter Sykacek, P. Sykacek, 1997, ESANN.

Outliers and Bayesian Inference

Peter Sykacek, P. Sykacek, 1998, NC.

RNA Interference in Ageing Research – A Mini-Review

David P. Kreil, P. Sykacek, N. Minois, 2010, Gerontology.

Comparison of Unsupervised Learning Methods for Natural Image Processing

Cristina Olaverri-Monreal, Wilfried Wöber, Peter Sykacek, 2019, Biodiversity Information Science and Standards.

Gene expression A friendly statistics package for microarray analysis

P. Sykacek, G. Micklem, R. Furlong, 2005 .