Dataset : Googlomics: Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks

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José Lages, Dima Shepelyansky, Andrei Zinovyev (2016): Googlomics: Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks. UTINAM. DOI:10.1101/096362

General metadata

Identifier : local : FR-18008901306731-2017-03-28-04 external : DOI:10.1101/096362
Description :
Signaling pathways represent parts of the global biological network which connects them into a seamless whole through complex direct and indirect (hidden) crosstalk whose structure can change during normal development or in a pathological conditions such as cancer. Advanced methods for characterizing the structure of the global directed causal network can shed light on the mechanisms of global cell reprogramming changing the distribution of possible signaling flows. We suggest a methodology, called Googlomics, for the analysis of the structure of directed biological networks using spectral analysis of their Google matrix. This approach uses parallels with quantum scattering theory, developed for processes in nuclear and mesoscopic physics and quantum chaos. We introduce the notion of reduced Google matrix in the context of the regulatory biological networks and demonstrate how its computation allows inferring hidden causal relations between the members of a signaling pathway or a functionally related group of genes. We investigate how the structure of hidden causal relations can be reprogrammed as the result of changes in the transcriptional network layer during cancerogenesis. The suggested Googlomics approach can be useful in various contexts for characterizing non-intuitive changes in the wiring of complex and large causal biological networks.
Disciplines :
Keywords :

Dates :
Data acquisition : from Dec 2015 to Dec 2016
Data issued : 22 Dec 2016
Metadata record :
Creation : 28 Mar 2017
Update : 30 Jan 2018

Language : English (eng)
Audience : Research


Taxonomic coverage :

  • Human
    Homo sapiens MSW (Human)

Administrative metadata

Data creatorsAffiliation
José LagesUTINAM
Dima ShepelyanskyLPT
Andrei ZinovyevInstitut Curie
Publisher : Institut UTINAM
Science contact : José Lages website e-mail
Computing contact : José Lages website e-mail
Project and funder :
  • ApliGoogle
    • Mission pour l’interdisciplinarité / Défi MASTODONS (CNRS)
Access : available

Technical metadata

Formats : application/pdf, text/csv, text/plain
Data acquisition methods :
Datatype : Dataset


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