MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals

@article{Peer2006MinRegAS,
  title={MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals},
  author={Dana Pe'er and Amos Tanay and Aviv Regev},
  journal={Journal of Machine Learning Research},
  year={2006},
  volume={7},
  pages={167-189}
}
In recent years, there has been a growing interest in applyin g Bayesian networks and their extensions to reconstruct regulatory networksfrom gene expression data. Since the gene expression domain involves a large number of variables and a limited num ber of samples, it poses both computational and statistical challenges to Bayesian network learning algorithms. Here we define a constrained family of Bayesian network structures suitabl e for this domain and devise an efficient search algorithm… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.
SHOWING 1-10 OF 25 CITATIONS

Similar Papers

Loading similar papers…