Ali Reza Khanteymoori

Gene Regulatory Network

-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Gene Regulatory Network

In recent years gene regulatory networks (GRNs) have attracted a lot of interest and many methods have been introduced for their statistical inference from gene expression data. Recently, Inference of large scale gene regulatory networks (GRNs) was made possible thanks to the availability of high-throughput gene expression data. The reconstruction of gene regulatory networks (GRNs) from high-throughput experimental data has been considered one of the most important issues in systems biology research. With the development of high throughput technology and the complexity of biological problems, we need to reconstruct GRNs that contain thousands of genes. However, when many existing algorithms are used to handle these large-scale problems, they will encounter two important issues: low accuracy and high computational cost. To overcome these difficulties, the main goal of this study is to design an effective parallel algorithm to infer large-scale GRNs based on high-performance parallel computing environments.

Current status

Many algorithms have been developed to derive GRN model from experimental data. These methods can be categorized into five main groups, namely Boolean networks, differential equation systems, Bayesian networks, evolutionary methods and information theoretic based methods. Currently, For GRN inference we focus on Bayesian Network structure learning and GPU implementation of the algorithms.

Contributing group members
Main publication

...

Software/Dataset
Related research area

...