Biology subjects

Lages, J.

Publications and source records attributed to Lages, J..

6 recordsLinked to original sources

bioRxiv2023

Identifying key species in meta-communities

O_LIWith the ongoing biodiversity crisis, identifying which species are of particular importance to prevent the extinction of other species has become a pressing issue. However, most approaches to detect these important species are made at a local (i.e, community) level, without considering the potential effect of species dispersion in a landscape. C_LIO_LIWe present a modified PageRank algorithm to determine the importance of species in meta-communities based on two sets of networks: food webs that depict local trophic interactions and landscape networks representing the movement of species across different habitat patches. C_LIO_LIWe show that (i) what is considered an important species changes between isolated communities and meta-communities and (ii) the importance of a species in a meta-community depends on the position of its habitat patch in the landscape network. C_LIO_LIOur results stress the need for a global consideration of space in the identification of important species. C_LI

ecology↗

bioRxiv2021

Statistical properties of the MetaCore network of protein-protein interactions

The MetaCore commercial database describes interactions of proteins and other chemical molecules and clusters in the form of directed network between these elements, viewed as nodes. The number of nodes goes beyond 40 thousands with almost 300 thousands links between them. The links have essentially bi-functional nature describing either activation or inhibition actions between proteins. We present here the analysis of statistical properties of this complex network applying the methods of the Google matrix, PageRank and CheiRank algorithms broadly used in the frame of the World Wide Web, Wikipedia, the world trade and other directed networks. We specifically describe the Ising PageRank approach which allows to treat the bi-functional type of protein-protein interactions. We also show that the developed reduced Google matrix algorithm allows to obtain an effective network of interactions inside a specific group of selected proteins. This method takes into account not only direct protein-protein interactions but also recover their indirect nontrivial couplings appearing due to summation over all the pathways passing via the global bi-functional network. The developed analysis allows to espablish an average action of each protein being more oriented to activation or inhibition. We argue that the described Google matrix analysis represents an efficient tool for investigation of influence of specific groups of proteins related to specific diseases.

bioinformatics↗

bioRxiv2019

Interactions of pharmaceutical companies with world countries, cancers and rare diseases from Wikipedia network analysis

Using the English Wikipedia network of more than 5 million articles we analyze interactions and interlinks between the 34 largest pharmaceutical companies, 195 world countries, 47 rare renal diseases and 37 types of cancer. The recently developed algorithm using a reduced Google matrix (REGOMAX) allows us to take account both of direct Markov transitions between these articles and also of indirect transitions generated by the pathways between them via the global Wikipedia network. This approach therefore provides a compact description of interactions between these articles that allows us to determine the friendship networks between them, as well as the PageRank sensitivity of countries to pharmaceutical companies and rare renal diseases. We also show that the top pharmaceutical companies in terms of their Wikipedia PageRank are not those with the highest market capitalization.

bioinformatics↗

bioRxiv2019

Wikipedia network analysis of cancer interactions and world influence

We apply the Google matrix algorithms for analysis of interactions and influence of 37 cancer types, 203 cancer drugs and 195 world countries using the network of 5 416 537 English Wikipedia articles with all their directed hyperlinks. The PageRank algorithm provides the importance order of cancers which has 60% and 70% overlaps with the top 10 cancers extracted from World Health Organization GLOBOCAN 2018 and Global Burden of Diseases Study 2017, respectively. The recently developed reduced Google matrix algorithm gives networks of interactions between cancers, drugs and countries taking into account all direct and indirect links between these selected 435 entities. These reduced networks allow to obtain sensitivity of countries to specific cancers and drugs. The strongest links between cancers and drugs are in good agreement with the approved medical prescriptions of specific drugs to specific cancers. We argue that this analysis of knowledge accumulated in Wikipedia provides useful complementary global information about interdependencies between cancers, drugs and world countries.

bioinformatics↗

bioRxiv2018

World Influence of Infectious Diseases from Wikipedia Network Analysis

We consider the network of 5 416 537 articles of English Wikipedia extracted in 2017. Using the recent reduced Google matrix (REGOMAX) method we construct the reduced network of 230 articles (nodes) of infectious diseases and 195 articles of world countries. This method generates the reduced directed network between all 425 nodes taking into account all direct and indirect links with pathways via the huge global network. PageRank and CheiRank algorithms are used to determine the most influential diseases with the top PageRank diseases being Tuberculosis, HIV/AIDS and Malaria. From the reduced Google matrix we determine the sensitivity of world countries to specific diseases integrating their influence over all their history including the times of ancient Egyptian mummies. The obtained results are compared with the World Health Organization (WHO) data demonstrating that the Wikipedia network analysis provides reliable results with up to about 80 percent overlap between WHO and REGOMAX analyses.

microbiology↗

bioRxiv2016

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

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 development or in pathological conditions. We suggest a novel methodology, called Googlomics, for the structural analysis of directed biological networks using spectral analysis of their Google matrices, using parallels with quantum scattering theory, developed for nuclear and mesoscopic physics and quantum chaos. We introduce the reduced Google matrix method for 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 rigorously characterizes complex systemic changes in the wiring of large causal biological networks.

systems biology↗