Johan Björkegren

15 papers A 1C 1Journal 10Unranked 3
YearRankTypeTitle / Venue / Authors
2020 J jnl
Model-based clustering of multi-tissue gene expression data.
Bioinform.
Pau Erola, Johan Björkegren, Tom Michoel
2014 J jnl
kruX: matrix-based non-parametric eQTL discovery.
BMC Bioinform.
Jianlong Qi, Hassan Asl, Johan Björkegren, Tom Michoel
2012 J jnl
Identifying the Relevant Nodes Without Learning the Model.
CoRR
José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér
2009 J jnl
An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity.
J. Mach. Learn. Res.
José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér
2009 J jnl
On reliable discovery of molecular signatures.
BMC Bioinform.
Roland Nilsson, Johan Björkegren, Jesper Tegnér
2007 J jnl
Consistent Feature Selection for Pattern Recognition in Polynomial Time.
J. Mach. Learn. Res.
Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér
2007 J jnl
Detecting multivariate differentially expressed genes.
BMC Bioinform.
Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér
2007 J jnl
Towards scalable and data efficient learning of Markov boundaries.
Int. J. Approx. Reason.
José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér
2006 J jnl
Detection of compound mode of action by computational integration of whole-genome measurements and genetic perturbations.
BMC Bioinform.
Kristofer Hallén, Johan Björkegren, Jesper Tegnér
2006 — conf
Evaluating Feature Selection for SVMs in High Dimensions.
ECML
Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér
2006 A conf
Identifying the Relevant Nodes Without Learning the Model.
UAI
José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér
2006 — conf
Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity.
Probabilistic Graphical Models
José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér
2005 — conf
Growing Bayesian network models of gene networks from seed genes.
ECCB/JBI
José M. Peña, Johan Björkegren, Jesper Tegnér
2005 J jnl
Learning dynamic Bayesian network models via cross-validation.
Pattern Recognit. Lett.
José M. Peña, Johan Björkegren, Jesper Tegnér
2005 C conf
Scalable, Efficient and Correct Learning of Markov Boundaries Under the Faithfulness Assumption.
ECSQARU
José M. Peña, Johan Björkegren, Jesper Tegnér