scholarly journals ESTIMATION OF NONLINEAR GENE REGULATORY NETWORKS VIA L1 REGULARIZED NVAR FROM TIME SERIES GENE EXPRESSION DATA

2008 ◽  
Author(s):  
KANAME KOJIMA ◽  
ANDRÉ FUJITA ◽  
TEPPEI SHIMAMURA ◽  
SEIYA IMOTO ◽  
SATORU MIYANO
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Data ◽  
Time Series Gene Expression ◽  
Gene Regulatory

scholarly journals Learning the structure of gene regulatory networks from time series gene expression data

BMC Genomics ◽  
2011 ◽  
Vol 12 (Suppl 5) ◽  
pp. S13 ◽  
Author(s):  
Haoni Li ◽  
Nan Wang ◽  
Ping Gong ◽  
Edward J Perkins ◽  
Chaoyang Zhang
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Data ◽  
Time Series Gene Expression ◽  
Gene Regulatory

scholarly journals Intelligent Framework with Controlled Behavior for Gene Regulatory Networks Reconstruction

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Superior Performance ◽  
Gene Interactions ◽  
Expression Data ◽  
Time Series Gene Expression ◽  
Gene Regulatory

Gene Regulatory Networks (GRNs) are the pioneering methodology for finding new gene interactions getting insights of the biological processes using time series gene expression data. It remains a challenge to study the temporal nature of gene expression data that mimic complex non-linear dynamics of the network. In this paper, an intelligent framework of recurrent neural network (RNN) and swarm intelligence (SI) based Particle Swarm Optimization (PSO) with controlled behaviour has been proposed for the reconstruction of GRN from time-series gene expression data. A novel PSO algorithm enhanced by human cognition influenced by the ideology of Bhagavad Gita is employed for improved learning of RNN. RNN guided by the proposed algorithm simulates the nonlinear and dynamic gene interactions to a greater extent. The proposed method shows superior performance over traditional SI algorithms in searching biologically plausible candidate networks. The strength of the method is verified by analyzing the small artificial network and real data of Escherichia coli with improved accuracy.

scholarly journals Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks

2021 ◽  
Author(s):  
Polina Suter ◽  
Jack Kuipers ◽  
Niko Beerenwinkel
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Predictive Accuracy ◽  
Dynamic Bayesian Networks ◽  
Expression Data ◽  
Gene Regulatory ◽  
Phenotypic Groups

Dynamic Bayesian networks (DBNs) can be used for the discovery of gene regulatory networks from time series gene expression data. Here, we suggest a strategy for learning DBNs from gene expression data by employing a Bayesian approach that is scalable to large networks and is targeted at learning models with high predictive accuracy. Our framework can be used to learn DBNs for multiple groups of samples and highlight differences and similarities in their gene regulatory networks. We learn these DBN models based on different structural and parametric assumptions and select the optimal model based on the cross-validated predictive accuracy. We show in simulation studies that our approach is better equipped to prevent overfitting than techniques used in previous studies. We applied the proposed DBN-based classification approach to two time series transcriptomic datasets from the Gene Expression Omnibus database, each comprising data from distinct phenotypic groups of the same tissue type. In the first case, we used DBNs to characterize responders and non-responders to anti-cancer therapy. In the second case, we compared normal to tumor cells of colorectal tissue. The classification accuracy reached by the DBN-based classifier for both datasets was higher than reported previously. For the colorectal cancer dataset, our analysis suggested that GRNs for cancer and normal tissues have a lot of differences, which are most pronounced in the neighborhoods of oncogenes and known cancer tissue markers. The identified differences in gene networks of cancer and normal cells may be used for the discovery of targeted therapies.

Inference of Gene Regulatory Networks by Topological Prior Information and Data Integration

Biotechnology ◽  
2019 ◽  
pp. 265-304
Author(s):  
David Correa Martins Jr. ◽  
Fabricio Martins Lopes ◽  
Shubhra Sankar Ray
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Prior Information ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Expression Data ◽  
Heterogeneous Data Sources ◽  
Gene Regulatory

The inference of Gene Regulatory Networks (GRNs) is a very challenging problem which has attracted increasing attention since the development of high-throughput sequencing and gene expression measurement technologies. Many models and algorithms have been developed to identify GRNs using mainly gene expression profile as data source. As the gene expression data usually has limited number of samples and inherent noise, the integration of gene expression with several other sources of information can be vital for accurately inferring GRNs. For instance, some prior information about the overall topological structure of the GRN can guide inference techniques toward better results. In addition to gene expression data, recently biological information from heterogeneous data sources have been integrated by GRN inference methods as well. The objective of this chapter is to present an overview of GRN inference models and techniques with focus on incorporation of prior information such as, global and local topological features and integration of several heterogeneous data sources.

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