Inferring Gene Regulatory Network from Bayesian Network Model Based on Re-Sampling
Abstract: Nowadays, gene chip
technology has rapidly produced a wealth of information about gene expression
activities. But the time-series expression data present a phenomenon that the
number of genes is in thousands and the number of experimental data is only a
few dozen. For such cases, it is difficult to learn network structure from such
data. And the result is not ideal. So it needs to take measures to expand the
capacity of the sample. In this paper, the Block bootstrap re-sampling method
is utilized to enlarge the small expression data. At the same time, we apply
“K2+T” algorithm to Yeast cell cycle gene expression data. Seeing from the
experimental results and comparing with the semi-fixed structure EM learning
algorithm, our proposed method is successful in constructing gene networks that
capture much more known relationships as well as several unknown relationships
which are likely to be novel.
Author: Qian Zhang, Xuedong
Zheng, Qiang Zhang, Changjun Zhou
Journal Code: jptkomputergg130041
