  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 unlabeled data for
training. We investigate inductive and transductive learning approaches, both
of which adopt an iterative procedure to obtain reliable negative training data
from the unlabeled data. We then apply our semi-supervised methods to gene
expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluate
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.
