Kim Dong-Chul, Wang Jiao, Liu Chunyu, Gao Jean
Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.
Beijing Genomics Institution at Wuhan, Wuhan 430075, China.
Biomed Res Int. 2014;2014:629697. doi: 10.1155/2014/629697. Epub 2014 Jun 9.
In order to elucidate the overall relationships between gene expressions and genetic perturbations, we propose a network inference method to infer gene regulatory network where single nucleotide polymorphism (SNP) is involved as a regulator of genes. In the most of the network inferences named as SNP-gene regulatory network (SGRN) inference, pairs of SNP-gene are given by separately performing expression quantitative trait loci (eQTL) mappings. In this paper, we propose a SGRN inference method without predefined eQTL information assuming a gene is regulated by a single SNP at most. To evaluate the performance, the proposed method was applied to random data generated from synthetic networks and parameters. There are three main contributions. First, the proposed method provides both the gene regulatory inference and the eQTL identification. Second, the experimental results demonstrated that integration of multiple methods can produce competitive performances. Lastly, the proposed method was also applied to psychiatric disorder data in order to explore how the method works with real data.
为了阐明基因表达与基因扰动之间的整体关系，我们提出了一种网络推断方法，用于推断涉及单核苷酸多态性（SNP）作为基因调节因子的基因调控网络。在大多数被称为SNP-基因调控网络（SGRN）推断的网络推断中，SNP-基因对是通过分别进行表达数量性状位点（eQTL）映射来给出的。在本文中，我们提出了一种无需预定义eQTL信息的SGRN推断方法，假设一个基因最多由一个SNP调控。为了评估性能，将所提出的方法应用于从合成网络和参数生成的随机数据。本文有三个主要贡献。第一，所提出的方法既提供了基因调控推断，又提供了eQTL识别。第二，实验结果表明，多种方法的整合可以产生具有竞争力的性能。最后，所提出的方法也应用于精神疾病数据，以探索该方法在真实数据上的工作方式。
