ArticleBMC bioinformatics2011
Fast network centrality analysis using GPUs.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed.
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- Bioinformatics and Next-Generation Data Analysis for Identification of Genes and Molecular Pathways Involved in Subjects with Diabetes and Obesity.Medicina (Kaunas, Lithuania) · 2023Article
- Identification and Interaction Analysis of Molecular Markers in Pancreatic Ductal Adenocarcinoma by Bioinformatics and Next-Generation Sequencing Data Analysis.Bioinformatics and biology insights · 2023Article
- Bioinformatics Analysis of Next Generation Sequencing Data Identifies Molecular Biomarkers Associated With Type 2 Diabetes Mellitus.Clinical medicine insights. Endocrinology and diabetes · 2023Article
- Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis.Scientific reports · 2022Article
- Integrated bioinformatics analysis reveals novel key biomarkers in diabetic nephropathy.SAGE open medicine · 2022Article
- Article
- TurboBC: A Memory Efficient and Scalable GPU Based Betweenness Centrality Algorithm in the Language of Linear Algebra.Proceedings of the ... ICPP Workshops on. International Conference on Parallel Processing Workshops · 2021Article
- Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis.BMC cardiovascular disorders · 2021Article
- Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus.Bioscience reports · 2021Article
- Investigation of candidate genes and mechanisms underlying obesity associated type 2 diabetes mellitus using bioinformatics analysis and screening of small drug molecules.BMC endocrine disorders · 2021Article
- Identification of hub genes related to the progression of type 1 diabetes by computational analysis.BMC endocrine disorders · 2021Article
- Identification of key pathways and genes in polycystic ovary syndrome via integrated bioinformatics analysis and prediction of small therapeutic molecules.Reproductive biology and endocrinology : RB&E · 2021Article
- Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking Studies.Frontiers in endocrinology · 2021Article
- Potential Molecular Mechanisms and Remdesivir Treatment for Acute Respiratory Syndrome Corona Virus 2 Infection/COVID 19 Through RNA Sequencing and Bioinformatics Analysis.Bioinformatics and biology insights · 2021Article
- Bioinformatics analyses of significant genes, related pathways, and candidate diagnostic biomarkers and molecular targets in SARS-CoV-2/COVID-19.Gene reports · 2020Article
- Article
- Identification of Important Invasion-Related Genes in Non-functional Pituitary Adenomas.Journal of molecular neuroscience : MN · 2019Article
- Identification of important invasion and proliferation related genes in adrenocortical carcinoma.Medical oncology (Northwood, London, England) · 2019Article
- Unravelling personalized dysfunctional gene network of complex diseases based on differential network model.Journal of translational medicine · 2015Article
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2 authors.
Funding
Abstract
backgroundWith the exploding volume of data generated by continuously evolving high-throughput technologies, biological network analysis problems are growing larger in scale and craving for more computational power. General Purpose computation on Graphics Processing Units (GPGPU) provides a cost-effective technology for the study of large-scale biological networks. Designing algorithms that maximize data parallelism is the key in leveraging the power of GPUs.
resultsWe proposed an efficient data parallel formulation of the All-Pairs Shortest Path problem, which is the key component for shortest path-based centrality computation. A betweenness centrality algorithm built upon this formulation was developed and benchmarked against the most recent GPU-based algorithm. Speedup between 11 to 19% was observed in various simulated scale-free networks. We further designed three algorithms based on this core component to compute closeness centrality, eccentricity centrality and stress centrality. To make all these algorithms available to the research community, we developed a software package gpu-fan (GPU-based Fast Analysis of Networks) for CUDA enabled GPUs. Speedup of 10-50× compared with CPU implementations was observed for simulated scale-free networks and real world biological networks.
conclusionsgpu-fan provides a significant performance improvement for centrality computation in large-scale networks. Source code is available under the GNU Public License (GPL) at http://bioinfo.vanderbilt.edu/gpu-fan/.
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