Selected publications most relevant to our current research are:
M. Esnaola, P. Puig, D. Gonzalez, R. Castelo and J.R. Gonzalez. A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments. BMC Bioinformatics, 14:254, 2013. [full text, Bioconductor package].
A. Roverato and R. Castelo. Learning undirected graphical models from multiple datasets with the generalized non-rejection rate. International Journal of Approximate Reasoning, 53(9):1326-1335, 2012. [abstract].
R. Castelo and A. Roverato. Inference of regulatory networks from microarray data with R and the Bioconductor package qpgraph. In Next Generation Microarray Bioinformatics, J. Wang, A.C. Tan and T. Tian, Eds., pp. 215-233, Methods in Molecular Biology Series, Humana Press, USA, ISBN 978-1-61779-399-8,2012. [publisher, preprint, Bioconductor package].
I. Tur and R. Castelo. Learning mixed graphical models from data with p larger than n. In Proceedings 27th Conference on Uncertainty in Artificial Intelligence, F.G. Cozman and A. Pfeffer, Eds., pp. 689-697, AUAI Press, ISBN 978-0-9749039-7-2, Barcelona, 2011. [PDF].
R. Castelo and A. Roverato. Reverse engineering molecular regulatory networks from microarray data with qp-graphs, Journal of Computational Biology, 16(2):213-227, 2009. [preprint, supplement, Bioconductor package].
R. Castelo and A. Roverato. A robust procedure for Gaussian graphical model search from microarray data with p larger than n, Journal of Machine Learning Research, 7(Dec):2621-2650, 2006. [abstract and PDF, R package]
R. Castelo and T. Kocka. On inclusion-driven learning of Bayesian Networks. Journal of Machine Learning Research, 4(Sep):527-574, 2003. [abstract and PDF]