Keedwell Edward, Narayanan Ajit
School of Engineering, Computer Science and Mathematics, Harrison Building, North Park Road, University of Exeter, Exeter, UK.
IEEE/ACM Trans Comput Biol Bioinform. 2005 Jul-Sep;2(3):231-42. doi: 10.1109/TCBB.2005.40.
Recent advances in biology (namely, DNA arrays) allow an unprecedented view of the biochemical mechanisms contained within a cell. However, this technology raises new challenges for computer scientists and biologists alike, as the data created by these arrays is often highly complex. One of the challenges is the elucidation of the regulatory connections and interactions between genes, proteins and other gene products. In this paper, a novel method is described for determining gene interactions in temporal gene expression data using genetic algorithms combined with a neural network component. Experiments conducted on real-world temporal gene expression data sets confirm that the approach is capable of finding gene networks that fit the data. A further repeated approach shows that those genes significantly involved in interaction with other genes can be highlighted and hypothetical gene networks and circuits proposed for further laboratory testing.
生物学领域的最新进展（即DNA阵列）使人们能够以前所未有的视角观察细胞内包含的生化机制。然而，这项技术给计算机科学家和生物学家都带来了新的挑战，因为这些阵列产生的数据往往高度复杂。其中一个挑战是阐明基因、蛋白质和其他基因产物之间的调控联系与相互作用。本文描述了一种新颖的方法，该方法利用遗传算法结合神经网络组件来确定时间基因表达数据中的基因相互作用。对真实世界的时间基因表达数据集进行的实验证实，该方法能够找到符合数据的基因网络。进一步的重复方法表明，可以突出那些与其他基因相互作用显著的基因，并提出假设的基因网络和回路以供进一步的实验室测试。
