Hamed Raed I, Ahson S I, Parveen R
Department of Computer Science, JMI University, New Delhi 110025, India.
J Integr Bioinform. 2010 Feb 4;7(1):439. doi: 10.2390/biecoll-jib-2010-113.
Gene Regulatory Networks are models of genes and gene interactions at the expression level. The advent of microarray technology has challenged computer scientists to develop better algorithms for modeling the underlying regulatory relationship in between the genes. Fuzzy system has an ability to search microarray datasets for activator/repressor regulatory relationship. In this paper, we present a fuzzy reasoning model based on the Fuzzy Petri Net. The model considers the regulatory triplets by means of predicting changes in expression level of the target based on input expression level. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. Through formalization of fuzzy reasoning, we propose an approach to construct a rulebased reasoning system. The experimental results show the proposed approach is feasible and acceptable to predict changes in expression level of the target gene.
基因调控网络是基因及其在表达水平上相互作用的模型。微阵列技术的出现促使计算机科学家开发更好的算法来对基因之间潜在的调控关系进行建模。模糊系统有能力在微阵列数据集中搜索激活剂/抑制剂调控关系。在本文中，我们提出了一种基于模糊Petri网的模糊推理模型。该模型通过基于输入表达水平预测目标表达水平的变化来考虑调控三元组。这种方法消除了经典模糊模型中可能的错误预测，从而为推断调控关系提供了更广阔的搜索空间。通过模糊推理的形式化，我们提出了一种构建基于规则的推理系统的方法。实验结果表明，所提出的方法对于预测目标基因表达水平的变化是可行且可接受的。
