Hawkins Troy, Chitale Meghana, Kihara Daisuke
Department of Biological Sciences, College of Science, Purdue University, West Lafayette, IN 47907, USA.
Mol Biosyst. 2008 Mar;4(3):223-31. doi: 10.1039/b718229e. Epub 2008 Jan 28.
Biological interpretation of large scale omics data, such as protein-protein interaction data and microarray gene expression data, requires that the function of many genes in a data set is annotated or predicted. Here the predicted function for a gene does not necessarily have to be a detailed biochemical function; a broad class of function, or low-resolution function, may be sufficient to understand why a set of genes shows the observed expression pattern or interaction pattern. In this Highlight, we focus on two recent approaches for function prediction which aim to provide large coverage in function prediction, namely omics data driven approaches and a thorough data mining approach on homology search results.
对大规模组学数据(如蛋白质-蛋白质相互作用数据和微阵列基因表达数据)进行生物学解读,需要对数据集中许多基因的功能进行注释或预测。这里,一个基因的预测功能不一定非得是详细的生化功能;一种宽泛的功能类别,即低分辨率功能,可能就足以理解为什么一组基因会呈现出观察到的表达模式或相互作用模式。在本简报中,我们重点关注两种近期的功能预测方法,它们旨在实现功能预测的广泛覆盖,即组学数据驱动方法以及对同源性搜索结果进行全面的数据挖掘方法。