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MultiPaths. A python framework for analyzing multi-layer biological networks using diffusion algorithms

 
: Marín-Llaó, Josep; Mubeen, Sarah; Perera-Lluna, Alexandre; Hofmann-Apitius, Martin; Picart-Armada, Sergio; Domingo-Fernández, Daniel

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Volltext urn:nbn:de:0011-n-6188857 (755 KByte PDF)
MD5 Fingerprint: de2f4ee5c555b27712028d7f68e3b756
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Erstellt am: 9.1.2021


Bioinformatics 37 (2021), Nr.1, S.137-139
ISSN: 1367-4803
ISSN: 1460-2059
ISSN: 1367-4811
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer SCAI ()
networks; systems biology; bioinformatic; Knowledge Graphs; algorithms; software

Abstract
High-throughput screening yields vast amounts of biological data which can be highly challenging to interpret. In response, knowledge-driven approaches emerged as possible solutions to analyze large datasets by leveraging prior knowledge of biomolecular interactions represented in the form of biological networks. Nonetheless, given their size and complexity, their manual investigation quickly becomes impractical. Thus, computational approaches, such as diffusion algorithms, are often employed to interpret and contextualize the results of high-throughput experiments. Here, we present MultiPaths, a framework consisting of two independent Python packages for network analysis. While the first package, DiffuPy, comprises numerous commonly used diffusion algorithms applicable to any generic network, the second, DiffuPath, enables the application of these algorithms on multi-layer biological networks. To facilitate its usability, the framework includes a command line interface, reproducible examples, and documentation. To demonstrate the framework, we conducted several diffusion experiments on three independent multi-omics datasets over disparate networks generated from pathway databases, thus, highlighting the ability of multi-layer networks to integrate multiple modalities. Finally, the results of these experiments demonstrate how the generation of harmonized networks from disparate databases can improve predictive performance with respect to individual resources.

: http://publica.fraunhofer.de/dokumente/N-618885.html