Al Qazlan Tuqyah Abdullah, Hamdi-Cherif Aboubekeur, Kara-Mohamed Chafia
Information Technology Department, Computer College, Qassim University, Buraydah 51452, Saudi Arabia.
Computer Science Department, Computer College, Qassim University, Buraydah 51452, Saudi Arabia.
ScientificWorldJournal. 2015;2015:148010. doi: 10.1155/2015/148010. Epub 2015 Mar 23.
To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs) inference. GRNs represent causal relationships between genes that have a direct influence, trough protein production, on the life and the development of living organisms and provide a useful contribution to the understanding of the cellular functions as well as the mechanisms of diseases. Fuzzy systems are based on handling imprecise knowledge, such as biological information. They provide viable computational tools for inferring GRNs from gene expression data, thus contributing to the discovery of gene interactions responsible for specific diseases and/or ad hoc correcting therapies. Increasing computational power and high throughput technologies have provided powerful means to manage these challenging digital ecosystems at different levels from cell to society globally. The main aim of this paper is to report, present, and discuss the main contributions of this multidisciplinary field in a coherent and structured framework.
为解决细胞层面最具挑战性的问题之一，本文综述了基因调控网络（GRNs）推理中使用的模糊方法。基因调控网络代表了基因之间的因果关系，这些关系通过蛋白质产生对生物体的生命和发育产生直接影响，并为理解细胞功能以及疾病机制提供了有益的帮助。模糊系统基于处理不精确的知识，如生物信息。它们为从基因表达数据推断基因调控网络提供了可行的计算工具，从而有助于发现导致特定疾病的基因相互作用和/或特设的纠正疗法。不断提高的计算能力和高通量技术为在全球范围内从细胞到社会的不同层面管理这些具有挑战性的数字生态系统提供了强大手段。本文的主要目的是在一个连贯且结构化的框架内报告、展示和讨论这一多学科领域的主要贡献。
