Inferring Gene Regulatory Networks from Expression Data using Ensemble Methods

High-throughput technologies for measuring gene expression made inferring of the genome-wide Gene Regulatory Networks an active field of research. Reverse-engineering of systems of transcriptional regulations became an important challenge in molecular and computational biology. Because such systems model dependencies between genes, they are important in understanding of cell behavior, and can potentially turn observed expression data into the new biological knowledge and practical applications. In this dissertation we introduce a set of algorithms, which infer networks of transcriptional regulations from variety of expression profiles with superior accuracy compared to the state-of-the-art techniques. The proposed methods make use of ensembles of trees, which became popular in many scientific fields, including genetics and bioinformatics. However, originally they were motivated from the perspective of classification, regression, and feature selection theory. In this study we exploit their relative variable importance measure as an indication of the presence or absence of a regulatory interaction between genes. We further analyze their predictions on a set of the universally recognized benchmark expression data sets, and achieve favorable results in compare with the state-of-the-art algorithms