title: Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors.
abstract: 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(k)). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the 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).
authors: Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña , Campus de Elviña, 15071, A Coruña, A Coruña, Spain.
authors: Aliuska
authors: A
authors: Duardo-Sánchez
authors: Cristian R
authors: CR
authors: Munteanu
authors: Pablo
authors: P
authors: Riera-Fernández
authors: Antonio
authors: A
authors: López-Díaz
authors: Alejandro
authors: A
authors: Pazos
authors: Humberto
authors: H
authors: González-Díaz
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D016208:Databases, Factual; D017753:Ecosystem; D007603:Jurisprudence; D008390:Markov Chains; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D017059:Models, Econometric; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012944:Social Support; D012984:Software