Institute of Molecular Biology, Genetics and Biotechnology, Biomedical Research Foundation, Academy of Athens, 4 Soranou Efesiou Street, Athens 11527, Greece.
BMC Bioinformatics. 2010 Mar 18;11:140. doi: 10.1186/1471-2105-11-140.
Inference of gene regulatory networks is a key goal in the quest for understanding fundamental cellular processes and revealing underlying relations among genes. With the availability of gene expression data, computational methods aiming at regulatory networks reconstruction are facing challenges posed by the data's high dimensionality, temporal dynamics or measurement noise. We propose an approach based on a novel multi-layer evolutionary trained neuro-fuzzy recurrent network (ENFRN) that is able to select potential regulators of target genes and describe their regulation type.
The recurrent, self-organizing structure and evolutionary training of our network yield an optimized pool of regulatory relations, while its fuzzy nature avoids noise-related problems. Furthermore, we are able to assign scores for each regulation, highlighting the confidence in the retrieved relations. The approach was tested by applying it to several benchmark datasets of yeast, managing to acquire biologically validated relations among genes.
The results demonstrate the effectiveness of the ENFRN in retrieving biologically valid regulatory relations and providing meaningful insights for better understanding the dynamics of gene regulatory networks. The algorithms and methods described in this paper have been implemented in a Matlab toolbox and are available from: http://bioserver-1.bioacademy.gr/DataRepository/Project_ENFRN_GRN/.
基因调控网络的推断是理解基本细胞过程和揭示基因之间潜在关系的关键目标。随着基因表达数据的可用性，旨在重建调控网络的计算方法面临着数据高维性、时间动态或测量噪声带来的挑战。我们提出了一种基于新型多层进化训练的神经模糊递归网络（ENFRN）的方法，该方法能够选择靶基因的潜在调控因子，并描述其调控类型。
我们的网络的递归、自组织结构和进化训练产生了一个优化的调控关系池，而其模糊性质避免了与噪声相关的问题。此外，我们能够为每个调控分配分数，突出了检索到的关系的置信度。该方法通过应用于酵母的几个基准数据集进行了测试，成功获得了基因之间具有生物学验证的关系。
结果表明，ENFRN 在检索具有生物学意义的调控关系方面是有效的，并为更好地理解基因调控网络的动态提供了有意义的见解。本文描述的算法和方法已在 Matlab 工具箱中实现，并可从以下网址获得：http://bioserver-1.bioacademy.gr/DataRepository/Project_ENFRN_GRN/。
