ArticleGigaScience2020
Pyntacle: a parallel computing-enabled framework for large-scale network biology analysis.
Article in GigaScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Deciphering Microbial Composition in Patients with Inflammatory Bowel Disease: Implications for Therapeutic Response to Biologic Agents.Microorganisms · 2024Article
- Identification of hub genes and immune infiltration in ulcerative colitis using bioinformatics.Scientific reports · 2023Article
- Considerations and challenges for sex-aware drug repurposing.Biology of sex differences · 2022Review
- Polympact: exploring functional relations among common human genetic variants.Nucleic acids research · 2022Article
- Beyond COVID-19 pandemic: Topology-aware optimization of vaccination strategy for minimizing virus spreading.Computational and structural biotechnology journal · 2022Article
- Connecting the dots: A practical evaluation of web-tools for describing protein dynamics as networks.Frontiers in bioinformatics · 2022Review
- Pyntacle: a parallel computing-enabled framework for large-scale network biology analysis.GigaScience · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSome natural systems are big in size, complex, and often characterized by convoluted mechanisms of interaction, such as epistasis, pleiotropy, and trophism, which cannot be immediately ascribed to individual natural events or biological entities but that are often derived from group effects. However, the determination of important groups of entities, such as genes or proteins, in complex systems is considered a computationally hard task.
resultsWe present Pyntacle, a high-performance framework designed to exploit parallel computing and graph theory to efficiently identify critical groups in big networks and in scenarios that cannot be tackled with traditional network analysis approaches.
conclusionsWe showcase potential applications of Pyntacle with transcriptomics and structural biology data, thereby highlighting the outstanding improvement in terms of computational resources over existing tools.
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Registered trials
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