Johan Björkegren

According to our database, Johan Björkegren authored at least 16 papers between 2005 and 2020.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2020

alona: a web server for single-cell RNA-seq analysis.

Oscar Franzén, Johan L. M. Björkegren

Bioinform., 2020

Model-based clustering of multi-tissue gene expression data.

Pau Erola, Johan Björkegren, Tom Michoel

Bioinform., 2020

2019

PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data.

Oscar Franzén, Li-Ming Gan, Johan L. M. Björkegren

Database J. Biol. Databases Curation, 2019

2014

kruX: matrix-based non-parametric eQTL discovery.

Jianlong Qi, Hassan Asl, Johan Björkegren, Tom Michoel

BMC Bioinform., 2014

2009

An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity.

José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér

J. Mach. Learn. Res., 2009

On reliable discovery of molecular signatures.

Roland Nilsson, Johan Björkegren, Jesper Tegnér

BMC Bioinform., 2009

2007

Consistent Feature Selection for Pattern Recognition in Polynomial Time.

Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér

J. Mach. Learn. Res., 2007

Towards scalable and data efficient learning of Markov boundaries.

José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér

Int. J. Approx. Reason., 2007

Detecting multivariate differentially expressed genes.

Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér

BMC Bioinform., 2007

2006

Detection of compound mode of action by computational integration of whole-genome measurements and genetic perturbations.

Kristofer Hallén, Johan Björkegren, Jesper Tegnér

BMC Bioinform., 2006

Identifying the Relevant Nodes Without Learning the Model.

José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér

Proceedings of the UAI '06, 2006

Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity.

José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér

Proceedings of the Third European Workshop on Probabilistic Graphical Models, 2006

Evaluating Feature Selection for SVMs in High Dimensions.

Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér

Proceedings of the Machine Learning: ECML 2006, 2006

2005

Learning dynamic Bayesian network models via cross-validation.

José M. Peña, Johan Björkegren, Jesper Tegnér

Pattern Recognit. Lett., 2005

Scalable, Efficient and Correct Learning of Markov Boundaries Under the Faithfulness Assumption.

José M. Peña, Johan Björkegren, Jesper Tegnér

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2005

Growing Bayesian network models of gene networks from seed genes.

José M. Peña, Johan Björkegren, Jesper Tegnér

Proceedings of the ECCB/JBI'05 Proceedings, Fourth European Conference on Computational Biology/Sixth Meeting of the Spanish Bioinformatics Network (Jornadas de BioInformática), Palacio de Congresos, Madrid, Spain, September 28, 2005