Gene regulatory network

Gene regulatory network identification from the yeast cell cycle based on a neuro-fuzzy system

B. H. Wang, Lim, J. W., Lim, J. S., Wang, B. H., Lim, J. W., and Lim, J. S., Gene regulatory network identification from the yeast cell cycle based on a neuro-fuzzy system, vol. 15, p. -, 2016.

Many studies exist for reconstructing gene regulatory networks (GRNs). In this paper, we propose a method based on an advanced neuro-fuzzy system, for gene regulatory network reconstruction from microarray time-series data. This approach uses a neural network with a weighted fuzzy function to model the relationships between genes. Fuzzy rules, which determine the regulators of genes, are very simplified through this method.

Identification of robust adaptation gene regulatory network parameters using an improved particle swarm optimization algorithm

X. N. Huang, Ren, H. P., Huang, X. N., and Ren, H. P., Identification of robust adaptation gene regulatory network parameters using an improved particle swarm optimization algorithm, vol. 15, p. -, 2016.

Robust adaptation is a critical ability of gene regulatory network (GRN) to survive in a fluctuating environment, which represents the system responding to an input stimulus rapidly and then returning to its pre-stimulus steady state timely. In this paper, the GRN is modeled using the Michaelis-Menten rate equations, which are highly nonlinear differential equations containing 12 undetermined parameters. The robust adaption is quantitatively described by two conflicting indices.

Data integration for identification of important transcription factors of STAT6-mediated cell fate decisions

M. Jargosch, Kröger, S., Gralinska, E., Klotz, U., Fang, Z., Chen, W., Leser, U., Selbig, J., Groth, D., Baumgrass, R., Jargosch, M., Kröger, S., Gralinska, E., Klotz, U., Fang, Z., Chen, W., Leser, U., Selbig, J., Groth, D., and Baumgrass, R., Data integration for identification of important transcription factors of STAT6-mediated cell fate decisions, vol. 15, p. -, 2016.

Data integration has become a useful strategy for uncovering new insights into complex biological networks. We studied whether this approach can help to delineate the signal transducer and activator of transcription 6 (STAT6)-mediated transcriptional network driving T helper (Th) 2 cell fate decisions. To this end, we performed an integrative analysis of publicly available RNA-seq data of Stat6-knockout mouse studies together with STAT6 ChIP-seq data and our own gene expression time series data during Th2 cell differentiation.

Constructing gene network based on biclusters of expression data

F. Liu, Yang, L., Tian, Z. Z., Wu, P., Sun, S. L., Liu, F., Yang, L., Tian, Z. Z., Wu, P., and Sun, S. L., Constructing gene network based on biclusters of expression data, vol. 15, p. -, 2016.

Two genes can be co-regulated and possibly have the similar function if they are similarly expressed, which provides a theoretical basis for construction of gene regulatory networks using gene expression data. Herein, a new method of gene regulatory network was constructed based on biclusters in this paper. Given a bicluster, this paper analyzes the correlation between genes in the clusters and then constructs the gene regulatory network by selecting genes with a correlation coefficient.

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