Comput Biol Chem - Using gene expression programming to infer gene regulatory networks from time-series data.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
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{ model(3480) simul(1196) paramet(876) }
{ data(2317) use(1299) case(1017) }
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{ method(1557) propos(1049) approach(1037) }
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{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
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{ group(2977) signific(1463) compar(1072) }
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{ method(2212) result(1239) propos(1039) }

Resumo

Gene regulatory networks inference is currently a topic under heavy research in the systems biology field. In this paper, gene regulatory networks are inferred via evolutionary model based on time-series microarray data. A non-linear differential equation model is adopted. Gene expression programming (GEP) is applied to identify the structure of the model and least mean square (LMS) is used to optimize the parameters in ordinary differential equations (ODEs). The proposed work has been first verified by synthetic data with noise-free and noisy time-series data, respectively, and then its effectiveness is confirmed by three real time-series expression datasets. Finally, a gene regulatory network was constructed with 12 Yeast genes. Experimental results demonstrate that our model can improve the prediction accuracy of microarray time-series data effectively.

Resumo Limpo

gene regulatori network infer current topic heavi research system biolog field paper gene regulatori network infer via evolutionari model base timeseri microarray data nonlinear differenti equat model adopt gene express program gep appli identifi structur model least mean squar lms use optim paramet ordinari differenti equat ode propos work first verifi synthet data noisefre noisi timeseri data respect effect confirm three real timeseri express dataset final gene regulatori network construct yeast gene experiment result demonstr model can improv predict accuraci microarray timeseri data effect

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