Di Camillo Barbara, Sanchez-Cabo Fatima, Toffolo Gianna, Nair Sreekumaran K, Trajanoski Zlatko, Cobelli Claudio
Information Engineering Department, University of Padova, Padova, 35131, Italy.
BMC Bioinformatics. 2005 Dec 1;6 Suppl 4(Suppl 4):S11. doi: 10.1186/1471-2105-6-S4-S11.
Reconstructing regulatory networks from gene expression profiles is a challenging problem of functional genomics. In microarray studies the number of samples is often very limited compared to the number of genes, thus the use of discrete data may help reducing the probability of finding random associations between genes.
A quantization method, based on a model of the experimental error and on a significance level able to compromise between false positive and false negative classifications, is presented, which can be used as a preliminary step in discrete reverse engineering methods. The method is tested on continuous synthetic data with two discrete reverse engineering methods: Reveal and Dynamic Bayesian Networks.
The quantization method, evaluated in comparison with two standard methods, 5% threshold based on experimental error and rank sorting, improves the ability of Reveal and Dynamic Bayesian Networks to identify relations among genes.
从基因表达谱重建调控网络是功能基因组学中一个具有挑战性的问题。在微阵列研究中，与基因数量相比，样本数量往往非常有限，因此使用离散数据可能有助于降低发现基因之间随机关联的概率。
提出了一种量化方法，该方法基于实验误差模型和能够在假阳性和假阴性分类之间进行折衷的显著性水平，可作为离散逆向工程方法的初步步骤。该方法通过两种离散逆向工程方法（Reveal和动态贝叶斯网络）在连续合成数据上进行了测试。
与两种标准方法（基于实验误差的5%阈值和秩排序）相比进行评估的量化方法，提高了Reveal和动态贝叶斯网络识别基因间关系的能力。
Zotero 插件