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Johan Björkegren
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2020 – today
- 2020
- [j11]Pau Erola
, Johan Björkegren
Model-based clustering of multi-tissue gene expression data. Bioinform. 36(6): 1807-1813 (2020) - [j10]Oscar Franzén, Johan L. M. Björkegren
alona: a web server for single-cell RNA-seq analysis. Bioinform. 36(12): 3910-3912 (2020)
2010 – 2019
- 2019
- [j9]Oscar Franzén
PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database J. Biol. Databases Curation 2019: baz046 (2019) - 2014
- [j8]Jianlong Qi, Hassan Asl, Johan Björkegren
kruX: matrix-based non-parametric eQTL discovery. BMC Bioinform. 15: 11 (2014) - 2012
- [i1]José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér:
Identifying the Relevant Nodes Without Learning the Model. CoRR abs/1206.6847 (2012)
2000 – 2009
- 2009
- [j7]Roland Nilsson
On reliable discovery of molecular signatures. BMC Bioinform. 10 (2009) - [j6]José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér:
An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity. J. Mach. Learn. Res. 10: 1071-1094 (2009) - 2007
- [j5]Roland Nilsson
Detecting multivariate differentially expressed genes. BMC Bioinform. 8 (2007) - [j4]José M. Peña, Roland Nilsson
Towards scalable and data efficient learning of Markov boundaries. Int. J. Approx. Reason. 45(2): 211-232 (2007) - [j3]Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér:
Consistent Feature Selection for Pattern Recognition in Polynomial Time. J. Mach. Learn. Res. 8: 589-612 (2007) - 2006
- [j2]Kristofer Hallén, Johan Björkegren
Detection of compound mode of action by computational integration of whole-genome measurements and genetic perturbations. BMC Bioinform. 7: 51 (2006) - [c5]Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér:
Evaluating Feature Selection for SVMs in High Dimensions. ECML 2006: 719-726 - [c4]José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér:
Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity. Probabilistic Graphical Models 2006: 247-254 - [c3]José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér:
Identifying the Relevant Nodes Without Learning the Model. UAI 2006 - 2005
- [j1]José M. Peña, Johan Björkegren
Learning dynamic Bayesian network models via cross-validation. Pattern Recognit. Lett. 26(14): 2295-2308 (2005) - [c2]José M. Peña, Johan Björkegren
Growing Bayesian network models of gene networks from seed genes. ECCB/JBI 2005: 229 - [c1]José M. Peña, Johan Björkegren, Jesper Tegnér:
Scalable, Efficient and Correct Learning of Markov Boundaries Under the Faithfulness Assumption. ECSQARU 2005: 136-147
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