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Titulo: The Rucker-Markov Invariants of Complex Bio-Systems: Applications in Parasitology and Neuroinformatics
Tipo: revista internacional
Fecha: 3,2013
Revista: BioSystems
JCR Journal; Impact Factor: 1.784
Volumen: 111(3)
Paginas: 199-207
ISSN: 0303-2647
doi: 10.1016/jbiosystems.2013.02.006
Pubmed ID: 23454544


Rücker's walk count (WC) indices are well-known topological indices (TIs) used in Chemoinformatics to quantify the molecular structure of drugs represented by a graph in Quantitative structure-activity/property relationship (QSAR/QSPR) studies. In this work, we introduce for the first time the higher-order (kth order) analogues (WCk) of these indices using Markov chains. In addition, we report new QSPR models for large complex networks of different Bio-Systems useful in Parasitology and Neuroinformatics. The new type of QSPR models can be used for model checking to calculate numerical scores S(Lij) for links Lij (checking or re-evaluation of network connectivity) in large networks of all these fields. The method may be summarized as follows: (i) first, the WCk(j) values are calculated for all jth nodes in a complex network already created; (ii) A linear discriminant analysis (LDA) is used to seek a linear equation that discriminates connected or linked (Lij=1) pairs of nodes experimentally confirmed from non-linked ones (Lij=0); (iii) The new model is validated with external series of pairs of nodes; (iv) The equation obtained is used to re-evaluate the connectivity quality of the network, connecting/disconnecting nodes based on the quality scores calculated with the new connectivity function. The linear QSPR models obtained yielded the following results in terms of overall test accuracy for re-construction of complex networks of different Bio-Systems: parasite-host networks (93.14%), NW Spain fasciolosis spreading networks (71.42/70.18%) and CoCoMac Brain Cortex co-activation network (86.40%). Thus, this work can contribute to the computational re-evaluation or model checking of connectivity (collation) in complex systems of any science field.

    .: SABIA :.  Sistemas Adaptativos y Bioinspirados en Inteligencia Artificial