title: Using gene expression programming to infer gene regulatory networks from time-series data.
abstract: 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.
authors.affiliation: College of Computer Science, Sichuan University, Chengdu 610065, PR China. Electronic address: zhangyongqingscu@hotmail.com.
authors.forename: Yongqing
authors.initials: Y
authors.lastname: Zhang
authors.forename: Yifei
authors.initials: Y
authors.lastname: Pu
authors.forename: Haisen
authors.initials: H
authors.lastname: Zhang
authors.forename: Yabo
authors.initials: Y
authors.lastname: Su
authors.forename: Lifang
authors.initials: L
authors.lastname: Zhang
authors.forename: Jiliu
authors.initials: J
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D030541:Databases, Genetic; D015870:Gene Expression; D053263:Gene Regulatory Networks; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D012441:Saccharomyces cerevisiae / Q000235:genetics; D013997:Time Factors
keywords: Gene expression programming; Gene regulatory networks; Least mean square; Ordinary differential equation