Using the minimum description length principle to reduce the rate of false positives of best-fit algorithms. Academic Article uri icon

abstract

  • The inference of gene regulatory networks is a core problem in systems biology. Many inference algorithms have been proposed and all suffer from false positives. In this paper, we use the minimum description length (MDL) principle to reduce the rate of false positives for best-fit algorithms. The performance of these algorithms is evaluated via two metrics: the normalized-edge Hamming distance and the steady-state distribution distance. Results for synthetic networks and a well-studied budding-yeast cell cycle network show that MDL-based filtering is more effective than filtering based on conditional mutual information (CMI). In addition, MDL-based filtering provides better inference than the MDL algorithm itself.

published proceedings

  • EURASIP J Bioinform Syst Biol

author list (cited authors)

  • Fang, J., Ouyang, H., Shen, L., Dougherty, E. R., & Liu, W.

citation count

  • 3

complete list of authors

  • Fang, Jie||Ouyang, Hongjia||Shen, Liangzhong||Dougherty, Edward R||Liu, Wenbin

publication date

  • December 2014