Information-theoretic inference of large transcriptional regulatory networks
EURASIP Journal on Bioinformatics and Systems Biology
Bayesian Inference and Optimal Design for the Sparse Linear Model
The Journal of Machine Learning Research
A Bayesian regression approach to the prediction of MHC-II binding affinity
Computer Methods and Programs in Biomedicine
Inference of an oscillating model for the yeast cell cycle
Discrete Applied Mathematics
Bayesian inference of gene regulatory networks using gene expression time series data
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
Biological network inference using redundancy analysis
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
On the distance concentration awareness of certain data reduction techniques
Pattern Recognition
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Motivation: There is currently much interest in reverse-engineering regulatory relationships between genes from microarray expression data. We propose a new algorithmic method for inferring such interactions between genes using data from gene knockout experiments. The algorithm we use is the Sparse Bayesian regression algorithm of Tipping and Faul. This method is highly suited to this problem as it does not require the data to be discretized, overcomes the need for an explicit topology search and, most importantly, requires no heuristic thresholding of the discovered connections. Results: Using simulated expression data, we are able to show that this algorithm outperforms a recently published correlation-based approach. Crucially, it does this without the need to set any ad hoc threshold on possible connections. Availability: Matlab code which allows all experimental results to be reproduced is available at http://www.dcs.gla.ac.uk/~srogers/reg_nets.html Contact: srogers@dcs.gla.ac.uk Supplementary information: Appendices and supplementary figures mentioned in the text can be found at http://www.dcs.gla.ac.uk/~srogers/reg_nets.html