Analysis of the GRNs Inference by Using Tsallis Entropy and a Feature Selection Approach

Abstract. An important problem in the bioinformatics field is to understand how genes are regulated and interact through gene networks. This knowledge can be helpful for many applications, such as disease treatment design and drugs creation purposes. For this reason, it is very important to uncover the functional relationship among genes and then to construct the gene regulatory network (GRN) from temporal expression data. However, this task usually involves data with a large number of variables and small number of observations. In this way, there is a strong motivation to use pattern recognition and dimensionality reduction approaches. In particular, feature selection is specially important in order to select the most important predictor genes that can explain some phenomena associated with the target genes. This work presents a first study about the sensibility of entropy methods regarding the entropy functional form, applied to the problem of topology recovery of GRNs. The general...

• Type: Conference
• Year: 2009
• Where: CIARP
• Authors: Fabrício Martins Lopes, Evaldo A. de Oliveira, Roberto M. Cesar
• Keywords: CIARP 2009, Gene Network, Image, Important Predictor Genes, Temporal Expression Data