J Chem Inf Model - Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors.

Tópicos

{ network(2748) neural(1063) input(814) }
{ framework(1458) process(801) describ(734) }
{ compound(1573) activ(1297) structur(1058) }
{ structur(1116) can(940) graph(676) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ measur(2081) correl(1212) valu(896) }
{ search(2224) databas(1162) retriev(909) }
{ studi(1119) effect(1106) posit(819) }
{ error(1145) method(1030) estim(1020) }
{ model(3480) simul(1196) paramet(876) }
{ gene(2352) biolog(1181) express(1162) }
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{ control(1307) perform(991) simul(935) }
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{ state(1844) use(1261) util(961) }
{ use(976) code(926) identifi(902) }
{ method(2212) result(1239) propos(1039) }
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{ imag(2830) propos(1344) filter(1198) }
{ extract(1171) text(1153) clinic(932) }
{ general(901) number(790) one(736) }
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{ model(2341) predict(2261) use(1141) }
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{ ehr(2073) health(1662) electron(1139) }
{ data(3008) multipl(1320) sourc(1022) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ cancer(2502) breast(956) screen(824) }
{ can(774) often(719) complex(702) }
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{ clinic(1479) use(1117) guidelin(835) }
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{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
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{ patient(2837) hospit(1953) medic(668) }
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{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
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{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
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{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

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).

Resumo Limpo

use numer paramet complex network analysi expand new field applic molecular level can use describ molecular structur chemic entiti protein interact metabol network howev applic restrict world molecul can extend studi macroscop nonliv system organ even legal social network hand develop field artifici intellig led formul comput algorithm whose design base structur function network biolog neuron algorithm call artifici neural network ann can use studi complex network sinc numer paramet encod inform network exampl centralitiesnod descriptor can use input ann wiener index w graph invari wide use chemoinformat quantifi molecular structur drug studi complex network work explor first time possibl use markov chain calcul analogu node distanc numbersw describ complex network point view node paramet call markovwien node descriptor order kth wk pleas note descriptor relat markovwien stochast process calcul wki valu high number node differ complex network use softwar minod network group accord field applic molecular network includ metabol reaction network mrns differ organ addit analyz biolog legal social network includ interact web databas biolog network iwdbn food web ecolog system spanish financi law network sfln calcul wki valu use input differ ann order discrimin correct node connect pattern incorrect random pattern miann model obtain present good valu sensitivityspecif mrns iwdbn sfln preliminari result promis point view first exploratori studi suggest use model extend highthroughput reevalu connect known complex network collat

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