Reverse engineering gene regulatory networks (GRNs), also known as network inference, refers to the process of reconstructing GRNs from gene expression data. Biologists model a GRN as a directed graph in which nodes represent genes and links show regulatory relationships between the genes. By predicting the links to infer a GRN, biologists can gain a better understanding of regulatory circuits ...

As a reverse engineering field, reconstructing a Gene Regulatory Network (GRN) from time series gene data has been a challenging issue in bioinformatics. This paper proposes a novel engineering framework that infers and reconstructs a gene regulatory network in terms of regulatory accuracy. Different from other statistical methods, the proposed framework uses features that represent the charact...

A Boolean network is a graphical model for representing and analyzing the behavior of gene regulatory networks (GRN). In this context, the accurate and efficient reconstruction of a Boolean network is essential for understanding the gene regulation mechanism and the complex relations that exist therein. In this paper we introduce an elegant and efficient algorithm for the reverse engineering of...

Genes are the basic blue print of life in an organism containing the physiological and behavioral characteristics. A gene regulatory network (GRN) is a set of genes, or parts of genes, that interact with each other to control a specific cell function. GRN inference is the reverse engineering approach to predict the biological network from the gene expression data. Biochemical system theory base...

Gene Regulatory Networks (GRN) provide systematic views of complex living systems, offering reliable and large-scale GRNs to identify disease candidate genes. A reverse engineering technique, Bayesian Model Averaging-based Networks (BMAnet), which ensembles all appropriate linear models to tackle uncertainty in model selection that integrates heterogeneous biological data sets is introduced. Us...

Cyber-physical systems consist of many hardware and software components. Over the lifetime these their components are often replaced or updated. To avoid integration problems, formal specifications component interface behavior crucial. Such a specification captures not only set provided operations but also order using them constraints on timing behavior. Usually expressed in terms state machine...

Computational reconstruction of gene regulatory networks (GRNs) from gene expression data is of great importance in systems biology. Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of computational GRN inference algorithm on benchmarks of simulated data. Tree-based methods, such as Random Forest, infer true regulators of a target gene in a...

background prostate cancer is a serious genetic disease known as the first widespread cancer in men, yet the molecular changes required for the cancer progression is not fully understood. availability of high-throughput gene expression data has led to the development of various computational methods for the identification of critical genes involved in the cancer. methods in this paper, we show ...