Semi-Supervised Prediction of Gene Regulatory Networks Using Machine Learning Algorithms

TL;DR

This paper introduces semi-supervised machine learning methods using SVM and RF to improve gene regulatory network prediction from gene expression data, outperforming existing supervised approaches.

Contribution

It presents novel semi-supervised approaches employing inductive and transductive learning for GRN prediction, utilizing unlabeled data to enhance accuracy.

Findings

Transductive learning outperforms inductive learning for both organisms.

Semi-supervised methods outperform existing supervised methods.

No significant performance difference between SVM and RF.

Abstract

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.