Institute of Systems Biology, Shanghai University, Shanghai, People's Republic of China.
PLoS One. 2011;6(9):e25297. doi: 10.1371/journal.pone.0025297. Epub 2011 Sep 28.
Given a regulatory pathway system consisting of a set of proteins, can we predict which pathway class it belongs to? Such a problem is closely related to the biological function of the pathway in cells and hence is quite fundamental and essential in systems biology and proteomics. This is also an extremely difficult and challenging problem due to its complexity. To address this problem, a novel approach was developed that can be used to predict query pathways among the following six functional categories: (i) "Metabolism", (ii) "Genetic Information Processing", (iii) "Environmental Information Processing", (iv) "Cellular Processes", (v) "Organismal Systems", and (vi) "Human Diseases". The prediction method was established trough the following procedures: (i) according to the general form of pseudo amino acid composition (PseAAC), each of the pathways concerned is formulated as a 5570-D (dimensional) vector; (ii) each of components in the 5570-D vector was derived by a series of feature extractions from the pathway system according to its graphic property, biochemical and physicochemical property, as well as functional property; (iii) the minimum redundancy maximum relevance (mRMR) method was adopted to operate the prediction. A cross-validation by the jackknife test on a benchmark dataset consisting of 146 regulatory pathways indicated that an overall success rate of 78.8% was achieved by our method in identifying query pathways among the above six classes, indicating the outcome is quite promising and encouraging. To the best of our knowledge, the current study represents the first effort in attempting to identity the type of a pathway system or its biological function. It is anticipated that our report may stimulate a series of follow-up investigations in this new and challenging area.
给定一个由一组蛋白质组成的调控途径系统，我们能否预测它属于哪种类别？这样的问题与途径在细胞中的生物学功能密切相关，因此在系统生物学和蛋白质组学中非常基础和重要。由于其复杂性，这也是一个极其困难和具有挑战性的问题。为了解决这个问题，开发了一种新的方法，可以用于预测以下六个功能类别中的查询途径：(i)“代谢”，(ii)“遗传信息处理”，(iii)“环境信息处理”，(iv)“细胞过程”，(v)“生物体系统”和 (vi)“人类疾病”。预测方法是通过以下步骤建立的：(i)根据伪氨基酸组成（PseAAC）的一般形式，将所涉及的每条途径表示为 5570-D（维）向量；(ii)根据途径系统的图形、生化和物理化学特性以及功能特性，从途径系统中提取一系列特征，为 5570-D 向量的每个分量赋值；(iii)采用最小冗余最大相关性（mRMR）方法进行预测。通过对由 146 个调控途径组成的基准数据集进行的 Jackknife 测试的交叉验证表明，我们的方法在识别上述六个类别中的查询途径方面的总体成功率达到了 78.8%，这表明结果相当有希望和鼓舞人心。据我们所知，目前的研究代表了首次尝试识别途径系统的类型或其生物学功能。我们预计，我们的报告可能会激发在这个新的和具有挑战性的领域进行一系列后续研究。
