Pyntacle: a parallel computing-enabled framework for large-scale network biology analysis | Zendy

Luca Parca | Zendy; Mauro Truglio | Zendy; Tommaso Biagini | Zendy; Stefano Castellana | Zendy; Francesco Petrizzelli | Zendy; Daniele Capocefalo | Zendy; Ferenc Jordán | Zendy; Massimo Carella | Zendy; Tommaso Mazza | Zendy

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Pyntacle: a parallel computing-enabled framework for large-scale network biology analysis

Author(s) -

Luca Parca,

Mauro Truglio,

Tommaso Biagini,

Stefano Castellana,

Francesco Petrizzelli,

Daniele Capocefalo,

Ferenc Jordán,

Massimo Carella,

Tommaso Mazza

Publication year - 2020

Publication title -

gigascience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.947

H-Index - 54

ISSN - 2047-217X

DOI - 10.1093/gigascience/giaa115

Subject(s) - exploit , computer science , systems biology , epistasis , pleiotropy , biological network , big data , gene regulatory network , complex network , theoretical computer science , data science , network analysis , graph , graph theory , network science , task (project management) , distributed computing , computational biology , data mining , biology , mathematics , gene , world wide web , physics , quantum mechanics , biochemistry , gene expression , computer security , management , combinatorics , economics , phenotype

Some 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.

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