Parallel network component analysis technique for gene regulatory network inference

2021 ◽  
Author(s):  
Dina Elsayad ◽  
Safwat Hamad ◽  
Howida A. Shedeed ◽  
Mohamed F. Tolba
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Network Inference ◽  
Component Analysis ◽  
Network Component Analysis ◽  
Gene Regulatory Network Inference ◽  
Analysis Technique ◽  
Parallel Network ◽  
Gene Regulatory ◽  
Network Component

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.

Large scale gene regulatory network inference with a multi-level strategy

2016 ◽  
Vol 12 (2) ◽  
pp. 588-597 ◽  
Author(s):  
Jun Wu ◽  
Xiaodong Zhao ◽  
Zongli Lin ◽  
Zhifeng Shao
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Large Scale ◽  
Network Inference ◽  
Biological Processes ◽  
Molecular Processes ◽  
Gene Regulatory Network Inference ◽  
Cell Functions ◽  
Multi Level ◽  
Gene Regulatory

Transcriptional regulation is a basis of many crucial molecular processes and an accurate inference of the gene regulatory network is a helpful and essential task to understand cell functions and gain insights into biological processes of interest in systems biology.

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