Progress on methods for inferring the gene networks from microarray data
基于微阵列数据的基因网络预测方法研究进展

DNA microarray technology makes it feasible to obtain quantitative measurements of expression of thousands of genes that present in a biological sample simultaneously. Genome-wide expression data generated from this technology are promising to uncover the complex relationships between these genes. Mathematical and computational methods are being developed in order to construct formal models of genetic interactions. There have been a number of attempts to model gene regulatory networks, including clustering, Boolean networks, Bayesian networks and differential equations. The present situation in computerized gene network reconstruction techniques was reviewed in detail. The specific advantages and disadvantages of these models were explained. Moreover, some valuable issues for future exploration in this area were indicated and discussed.