Date of Award

Spring 2015

Document Type


Degree Name

Master of Science in Bioinformatics - (M.S.)


Computer Science

First Advisor

Jason T. L. Wang

Second Advisor

Usman W. Roshan

Third Advisor

Dimitri Theodoratos


Obtaining gene regulatory networks (GRNs) from expression data is a challenging and crucial task. Many computational methods and algorithms have been developed to infer gene networks for gene expression data, which are usually obtained from microarray experiments. A gene network is a method to depict the relation among clusters of genes. To infer gene networks, the unsupervised method is used in this study. The two types of data used are time-series data and steady-state data. The data is analyzed using various tools containing different algorithms and concepts. GRNs from time-series data tools are obtained using the Time-delayed Algorithm for the Reconstruction of Accurate Cellular Networks (TD-ARACNe), the Bayesian Network Inference with Java Objects (BANJO), and causality. For steady-state data tools such as ARACNe, Gene Network Inference with Ensemble of trees (GENIE3), Context Likelihood or Relatedness Network (CLR), and Maximum Relevance Minimum Redundancy (MRNET) are used. The performance of time-series data as well as steady-state data based tool algorithms is compared by calculating their accuracy. The accuracy is calculated by comparing gene interactions between predicted and true networks. From the experimental studies it was found that the TD-ARACNe gives the highest accuracy on time-series gene expression data while for steady-state data, the ARACNe tool gives the highest accuracy. Overall, these analyses suggest that the suitability of the tools depends on the types of gene expression data available.