Reverse engineering gene networks using singular value decomposition and robust regression
Reverse engineering gene networks using singular value decomposition and robust regression
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DOI：
10.1073/pnas.092576199
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发表时间：
2002-04-30
影响因子：
11.1
通讯作者：
Collins, JJ
中科院分区：
文献类型：
--
作者：
Yeung, MKS;Tegnér, J;Collins, JJ
我们建议使用微阵列实验中相对少量的基因表达数据在基因组范围内反向工程基因网络进行反向工程基因网络。我们的方法基于经验观察，即这种网络通常较大且稀疏。它使用奇异的值分解来构建候选解决方案系列，然后使用强大的回归来识别最可能的解决方案的解决方案。我们的算法具有O（log n）采样复杂性和O（n-4）计算复杂性。我们在模型基因网络的Numero实验中测试和验证我们的方法。
We propose a scheme to reverse-engineer gene networks on a genome-wide scale using a relatively small amount of gene expression data from microarray experiments. Our method is based on the empirical observation that such networks are typically large and sparse. It uses singular value decomposition to construct a family of candidate solutions and then uses robust regression to identify the solution with the smallest number of connections as the most likely solution. Our algorithm has O(log N) sampling complexity and O(N-4) computational complexity. We test and validate our approach in a series of in numero experiments on model gene networks.