Inferring Gene Regulatory Network from Bayesian Network Model Based on Re-Sampling
Inferring Gene Regulatory Network from Bayesian Network Model Based on Re-Sampling
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DOI：
10.12928/telkomnika.v11i1.907
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发表时间：
2013-03
期刊：
影响因子：
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通讯作者：
Qian Zhang;Xuedong Zheng;Qiang Zhang;Changjun Zhou
中科院分区：
文献类型：
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作者：
Qian Zhang;Xuedong Zheng;Qiang Zhang;Changjun Zhou
目前，基因芯片技术已迅速产生了丰富的基因表达活动的信息。但时序表达数据呈现出基因数量以千计而实验数据只有几十个的现象。对于这种情况，很难从这些数据中学习网络结构。结果并不理想。因此需要采取措施扩大样本容量。本文利用Block bootstrap重采样方法对小样本表达数据进行放大。同时，将“K2+T”算法应用于酵母细胞周期基因表达数据。从实验结果来看，与半固定结构EM学习算法相比，我们提出的方法是成功的，在构建基因网络，捕获更多的已知关系，以及一些未知的关系，这可能是新的。
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 . A nd 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.