## Modeling Complex Metabolic Reactions, Ecological Systems, and Financial and Legal Networks with MIANN Models Based on Markov-Wiener Node Descriptors

2014-01-27T00:00:00Z (GMT)
by

The
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*k*^{th}(*W*). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the_{k}*W*_{k}(*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*W*_{k}(*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).#### Categories

#### License

CC BY-NC 4.0