J Chem Inf Model - New Markov-autocorrelation indices for re-evaluation of links in chemical and biological complex networks used in metabolomics, parasitology, neurosciences, and epidemiology.

Tópicos

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Resumo

The development of new methods for the computational re-evaluation of links in chemical and biological complex networks is very important to save time and resources. The Moreau-Broto autocorrelation indices (MBis) are well-known topological indices (TIs) used in QSAR/QSPR studies to encode the structural information contained in molecular graphs. In addition, MBis and similar autocorrelation measures have been used to study other systems like, for example, proteins. In the present work, MBis are combined with Markov chains to develop a general class of stochastic MBis of order k (MB(k)) that is used to encode the structural information contained in different types of large complex networks. The MB(k) values obtained for the nodes (centralities) of these networks are used as input variables to seek QSPR-like equations (by means of linear discriminant analysis) in which the outputs are numerical scores S(L(ij)) that allow us to discriminate between connected and nonconnected nodes and therefore re-evaluate the connectivity of the whole network. The models developed in this work produced the following results in terms of overall accuracy for network reconstruction: metabolic networks (72.10%), parasite-host networks (88.70%), CoCoMac brain cortex coactivation network (81.89%), and fasciolosis spreading network (86.39%).

Resumo Limpo

develop new method comput reevalu link chemic biolog complex network import save time resourc moreaubroto autocorrel indic mbis wellknown topolog indic tis use qsarqspr studi encod structur inform contain molecular graph addit mbis similar autocorrel measur use studi system like exampl protein present work mbis combin markov chain develop general class stochast mbis order k mbk use encod structur inform contain differ type larg complex network mbk valu obtain node central network use input variabl seek qsprlike equat mean linear discrimin analysi output numer score slij allow us discrimin connect nonconnect node therefor reevalu connect whole network model develop work produc follow result term overal accuraci network reconstruct metabol network parasitehost network cocomac brain cortex coactiv network fasciolosi spread network

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