Gene selection

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.

Locally linear embedding and neighborhood rough set-based gene selection for gene expression data classification

L. Sun, Xu, J. - C., Wang, W., Yin, Y., Sun, L., Xu, J. - C., Wang, W., and Yin, Y., Locally linear embedding and neighborhood rough set-based gene selection for gene expression data classification, vol. 15, p. -, 2016.

Cancer subtype recognition and feature selection are important problems in the diagnosis and treatment of tumors. Here, we propose a novel gene selection approach applied to gene expression data classification. First, two classical feature reduction methods including locally linear embedding (LLE) and rough set (RS) are summarized. The advantages and disadvantages of these algorithms were analyzed and an optimized model for tumor gene selection was developed based on LLE and neighborhood RS (NRS).

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