Inference of gene regulatory networks by means of dynamic differential Bayesian networks and nonparametric regression.
We propose a dynamic differential Bayesian networks (DDBNs) and nonparametric regression model. This model is an extended model of traditional dynamic Bayesian networks (DBNs), which can incorporate temporal information in a natural way and directly handle real-valued data obtained from microarrays without any transformation. In addition, it can cope with differential information between gene expression levels, without any loss to the traditional advantage, i.e., the capability of estimating non-linear relationships between genes. We apply DDBNs to analyze simulated data and real data, i.e., Saccharomyces cerevisiae cell cycle gene expression data. We have confirmed the effectiveness of our approach in the sense that some edges have been successfully detected only by DDBNs, not by DBNs.
Sugimoto
Naoya
N
Department of Fronteir Informatics, Graduate School of Frontier Science, University of Tokyo, Japan. sugimoto@iba.k.u-tokyo.ac.jp
Iba
Hitoshi
H
D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal; D008433:Mathematics; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D015398:Signal Transduction; D013997:Time Factors