Patel Nihir, Wang Jason T L
Bioinformatics Program, New Jersey Institute of Technology, Newark, NJ 07102, USA.
J Biosci. 2015 Oct;40(4):731-40. doi: 10.1007/s12038-015-9558-9.
Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.
利用计算方法从基因表达数据预测基因调控网络（GRN）是一项具有挑战性的任务。许多研究使用无监督方法来完成这项任务；然而，由于缺乏训练数据，此类方法通常预测准确率较低。在本文中，我们通过利用两种机器学习算法，即支持向量机（SVM）和随机森林（RF），提出了用于GRN预测的半监督方法。半监督方法利用未标记数据进行训练。我们研究了归纳学习和转导学习方法，这两种方法都采用迭代过程从未标记数据中获得可靠的负训练数据。然后，我们将半监督方法应用于大肠杆菌和酿酒酵母的基因表达数据，并使用表达数据评估我们方法的性能。我们的分析表明，对于这两种生物体，转导学习方法优于归纳学习方法。然而，在SVM和RF的性能方面没有发现确凿的差异。实验结果还表明，对于这两种生物体，所提出的半监督方法比现有的监督方法表现更好。
