The completion of Human Genome Project has been recognized as a great achievement in biomedical study; it not only provides information regarding human genes but also provides a new way to study human diseases such as cancers. How to analyze and interpret the results from massive amounts of gene expression profiles generated from high-throughput techniques such as microarray experiments has become a major challenge in the post-genomic research era. In this paper, we present a gene network analysis system (GNAS) that can not only generate gene networks of yeast cell cycle from experimental microarray data but can also compare the performance of gene networks using five different Bayesian network algorithms. The system utilizes both powerful MatLab processing ability and LabVIEW dynamic interfaces in a single platform. The gene networks of the yeast cell cycle were constructed based on five Bayesian network algorithms from four different microarrays of experimental data. Performance, such as computer time, sensitivity, and precision, was compared for these five algorithms. To our knowledge, this is the first design approach of its kind to be used in the study of gene networks.