scholarly journals Gene regulatory network construction identified NFYA as a diffuse subtype-specific prognostic factor in gastric cancer

2018 ◽  
Author(s):  
Bin Cao ◽  
Yu Zhao ◽  
Zheng Zhang ◽  
Hengcun Li ◽  
Jie Xing ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Prognostic Factor ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Network Construction ◽  
Gene Regulatory

scholarly journals A Review of Integration Strategies to Support Gene Regulatory Network Construction

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Hailin Chen ◽  
Vincent VanBuren
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Data Sources ◽  
Second Phase ◽  
Network Construction ◽  
Structural Integration ◽  
Analytic Integration ◽  
Gene Regulatory ◽  
Two Phases ◽  
Integration Strategies

Gene regulatory network (GRN) construction is a central task of systems biology. Integration of different data sources to infer and construct GRNs is an important consideration for the success of this effort. In this paper, we will discuss distinctive strategies of data integration for GRN construction. Basically, the process of integration of different data sources is divided into two phases: the first phase is collection of the required data and the second phase is data processing with advanced algorithms to infer the GRNs. In this paper these two phases are called “structural integration” and “analytic integration,” respectively. Compared with the nonintegration strategies, the integration strategies perform quite well and have better agreement with the experimental evidence.

scholarly journals Experimental approaches for gene regulatory network construction: The chick as a model system

genesis ◽  
2012 ◽  
Vol 51 (5) ◽  
pp. 296-310 ◽  
Author(s):  
Andrea Streit ◽  
Monica Tambalo ◽  
Jingchen Chen ◽  
Timothy Grocott ◽  
Maryam Anwar ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Model System ◽  
Network Construction ◽  
Experimental Approaches ◽  
Gene Regulatory

A review on the computational approaches for gene regulatory network construction

2014 ◽  
Vol 48 ◽  
pp. 55-65 ◽  
Author(s):  
Lian En Chai ◽  
Swee Kuan Loh ◽  
Swee Thing Low ◽  
Mohd Saberi Mohamad ◽  
Safaai Deris ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Network Construction ◽  
Computational Approaches ◽  
Gene Regulatory

Hybrid High-Performance Computing Algorithm for Gene Regulatory Network

2021 ◽  
Vol 13 (1) ◽  
pp. 52-64
Author(s):  
Dina Elsayad ◽  
Safawat Hamad ◽  
Howida Abd-Alfatah Shedeed ◽  
Mohamed Fahmy Tolba
Keyword(s):  
High Performance Computing ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
High Performance ◽  
Network Inference ◽  
Component Analysis ◽  
Gene Regulatory Network Inference ◽  
Network Construction ◽  
Gene Regulatory ◽  
Performance Computing

This paper presents a parallel algorithm for gene regulatory network construction, hereby referred to as H2pcGRN. The construction of gene regulatory network is a vital methodology for investigating the genes interactions' topological order, annotating the genes functionality and demonstrating the regulatory process. One of the approaches for gene regulatory network construction techniques is based on the component analysis method. The main drawbacks of component analysis-based algorithms are its intensive computations that consume time. Despite these drawbacks, this approach is widely applied to infer the regulatory network. Therefore, introducing parallel techniques is indispensable for gene regulatory network inference algorithms. H2pcGRN is a hybrid high performance-computing algorithm for gene regulatory network inference. The proposed algorithm is based on both the hybrid parallelism architecture and the generalized cannon's algorithm. A variety of gene datasets is used for H2pcGRN assessment and evaluation. The experimental results indicated that H2pcGRN achieved super-linear speedup, where its computational speedup reached 570 on 256 processing nodes.

Research on Gastric Cancer’s Drug-resistant Gene Regulatory Network Model

2020 ◽  
Vol 15 (3) ◽  
pp. 225-234
Author(s):  
Zhi Li ◽  
Tianyue Zhang ◽  
Haojie Lei ◽  
Liyan Wei ◽  
Yuanning Liu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Drug Resistance ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Differentially Expressed Gene ◽  
Multi Drug Resistance ◽  
Core Network ◽  
Valuable Data ◽  
Gene Regulatory ◽  
Primary Gene

Objective: Based on bioinformatics, differentially expressed gene data of drug-resistance in gastric cancer were analyzed, screened and mined through modeling and network modeling to find valuable data associated with multi-drug resistance of gastric cancer. Methods: First, data sets were preprocessed from three aspects: data processing, data annotation and classification, and functional clustering. Secondly, based on the preprocessed data, each classified primary gene regulatory network was constructed by mining interactions among the genes. This paper computed the values of each node in each classified primary gene regulatory network and ranked these nodes according to their scores. On the basis of this, the appropriate core node was selected and the corresponding core network was developed. Results and Conclusion:: Finally, core network modules were analyzed, which were mined. After the correlation analysis, the result showed that the constructed network module had 20 core genes. This module contained valuable data associated with multi-drug resistance in gastric cancer.

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