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Modeling Complex Metabolic Reactions, Ecological Systems, and Financial and Legal Networks with MIANN Models Based on Markov-Wiener Node Descriptors
journal contribution
posted on 2014-01-27, 00:00 authored by Aliuska Duardo-Sánchez, Cristian R. Munteanu, Pablo Riera-Fernández, Antonio López-Díaz, Alejandro Pazos, Humberto González-DíazThe
use of numerical parameters in Complex Network analysis is expanding
to new fields of application. At a molecular level, we can use them
to describe the molecular structure of chemical entities, protein
interactions, or metabolic networks. However, the applications are
not restricted to the world of molecules and can be extended to the
study of macroscopic nonliving systems, organisms, or even legal or
social networks. On the other hand, the development of the field of
Artificial Intelligence has led to the formulation of computational
algorithms whose design is based on the structure and functioning
of networks of biological neurons. These algorithms, called Artificial
Neural Networks (ANNs), can be useful for the study of complex networks,
since the numerical parameters that encode information of the network
(for example centralities/node descriptors) can be used as inputs
for the ANNs. The Wiener index (W) is a graph invariant
widely used in chemoinformatics to quantify the molecular structure
of drugs and to study complex networks. In this work, we explore for
the first time the possibility of using Markov chains to calculate
analogues of node distance numbers/W to describe
complex networks from the point of view of their nodes. These parameters
are called Markov-Wiener node descriptors of order kth (Wk). Please, note that
these descriptors are not related to Markov-Wiener stochastic processes.
Here, we calculated the Wk(i) values for a very high number of nodes (>100,000)
in more than 100 different complex networks using the software MI-NODES.
These networks were grouped according to the field of application.
Molecular networks include the Metabolic Reaction Networks (MRNs)
of 40 different organisms. In addition, we analyzed other biological
and legal and social networks. These include the Interaction Web Database
Biological Networks (IWDBNs), with 75 food webs or ecological systems
and the Spanish Financial Law Network (SFLN). The calculated Wk(i) values
were used as inputs for different ANNs in order to discriminate correct
node connectivity patterns from incorrect random patterns. The MIANN
models obtained present good values of Sensitivity/Specificity (%):
MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary
results are very promising from the point of view of a first exploratory
study and suggest that the use of these models could be extended to
the high-throughput re-evaluation of connectivity in known complex
networks (collation).
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Metabolic Reaction Networksprotein interactionsMRN75 food websEcological SystemsArtificial Neural Networkschemical entitiesnode connectivity patternsIWDBNmacroscopic nonliving systemsmodeling Complex Metabolic ReactionsLegal NetworksWiener indexMolecular networksComplex Network analysisInteraction Web DatabaseMIANN Modelsorder kthSpanish Financial Law Networkencode informationSFLNMarkov chainsMIANN modelsArtificial Intelligence
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