Brief. Bioinformatics - Biological network motif detection: principles and practice.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
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{ problem(2511) optim(1539) algorithm(950) }
{ method(1969) cluster(1462) data(1082) }
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{ research(1085) discuss(1038) issu(1018) }
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{ survey(1388) particip(1329) question(1065) }
{ network(2748) neural(1063) input(814) }
{ general(901) number(790) one(736) }
{ system(1050) medic(1026) inform(1018) }
{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ cancer(2502) breast(956) screen(824) }
{ imag(1947) propos(1133) code(1026) }
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{ state(1844) use(1261) util(961) }
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{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
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{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Network motifs are statistically overrepresented sub-structures (sub-graphs) in a network, and have been recognized as 'the simple building blocks of complex networks'. Study of biological network motifs may reveal answers to many important biological questions. The main difficulty in detecting larger network motifs in biological networks lies in the facts that the number of possible sub-graphs increases exponentially with the network or motif size (node counts, in general), and that no known polynomial-time algorithm exists in deciding if two graphs are topologically equivalent. This article discusses the biological significance of network motifs, the motivation behind solving the motif-finding problem, and strategies to solve the various aspects of this problem. A simple classification scheme is designed to analyze the strengths and weaknesses of several existing algorithms. Experimental results derived from a few comparative studies in the literature are discussed, with conclusions that lead to future research directions.

Resumo Limpo

network motif statist overrepres substructur subgraph network recogn simpl build block complex network studi biolog network motif may reveal answer mani import biolog question main difficulti detect larger network motif biolog network lie fact number possibl subgraph increas exponenti network motif size node count general known polynomialtim algorithm exist decid two graph topolog equival articl discuss biolog signific network motif motiv behind solv motiffind problem strategi solv various aspect problem simpl classif scheme design analyz strength weak sever exist algorithm experiment result deriv compar studi literatur discuss conclus lead futur research direct

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