Wikipedia network analysis of cancer interactions and world influence

Abstract

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 a ranking of cancers which has 60% and 70% overlaps with the top 10 deadliest 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.

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

“…A useful additional characteristic provided by the G R matrix is the sensitivity of the PageRank probability to the variation of a specific link between a pair of nodes chosen among the N r nodes of interest. The useful results obtained with this method have been demonstrated in [15–17, 19, 20]. The PageRank sensitivity D ( j → k, i ) of a node i to the matrix element G R jk is …”

“…We consider the English Wikipedia edition as at May 2017 with N = 5 416 537 articles (nodes) and N l = 122 232 932 hyperlinks between articles. This network has also been considered in [16, 17, 19, 20]. For the REGOMAX analysis, we select N c = 195 world countries (see the list and PageRank order in [19, 20]), the N ph = 34 largest pharmaceutical companies (see Table 1), N rd = 47 rare renal diseases (see Table 2), and N cr = 37 types of cancer listed in [20].…”

“…The REGOMAX approach has also been used for networks of protein-protein interactions [18] and for Wikipedia networks in order to determine the influence in Wikipedia of infectious diseases [19], drugs and cancers [20]. Here, we extend this approach to analyze the influence and mutual interactions in Wikipedia of the world’s 34 largest biotechnology and pharmaceutical companies [2].…”

Collective intelligence defines biological functions in Wikipedia as communities in the hidden protein connection network

“…18 Studying the large graph of Wikipedia hyperlinks with a focus on a particular subset of pages can provide 19 interesting insights about certain topics. Thus, for example, Wikipedia networks were explored to establish the 20 top historical figures of human history over 15 centuries [11], the geopolitical relations between countries [9], 21 the leading world universities [7], world influence of infectious and cancer diseases [27,28]. Hierarchical 22 structure of Wikipedia was revealed through application of network community detection algorithms [20].…”

“…The efficiency of the REGOMAX approach has been demonstrated for various Wikipedia networks 300 [7,9,13,27,28], protein networks from SIGNOR database [19], and the multiproduct world trade network 301 from UN COMTRADE database [7]. For the networks of hidden protein connections we applied Markov Clustering Algorithm (MCL) implemented 312 in ClusterMaker plugin for Cytoscape [23] with default parameters (granularity=2.0, edge weight cutoff=1.0, 313 number of iterations=16, maximum residual value=0.001).…”