Scientists are nowadays providing great awareness about microarray gene expression dataset. Researches tell that data mining fails to recognize the most important biological associations between genes. Recently biological information mining using clustering techniques were used for the analytical evaluation of gene expression. There are many challenges exist in the existing methods. Optimizatio...

Gene expression data play an important role in the development of efficient cancer diagnoses and classification. The genes identified are subsequently used to classify independent test set samples. The different feature selection methods are investigated and most frequent features are selected among all methods. This paper provides gene selection strategies for multiclass classification that ca...

It is generally assumed that stabilizing selection promoting a phenotypic optimum acts to shape variation in quantitative traits across individuals and species. Although gene expression represents an intensively studied molecular phenotype, the extent to which stabilizing selection limits divergence in gene expression remains contentious. In this study, we present a theoretical framework for th...

Microarray gene expression data can be analyzed for diagnosis of cancer and its stage. It usually concerns a very large number of variables relative to a small number of observations. This makes application of data mining techniques difficult and so to reduce the data dimensionality some pre-processing technique needs to be used. In this paper dataset used for analysis is about lung cancer cons...

conclusions this study indicated that the optimization of gene transfer increases the expression of igf-1. high concentrations of igf-1, in combination with cell therapy, have a significant effect on wound healing. results in vitro, the maximum expression of igf1 (96.95 pg/ml) in transfected fibroblast cells was 24 hours after gene transfer. in vivo, it was clear that igf-1 gene therapy caused ...

Genetic Algorithms are efficient search and optimization techniques inspired by natural evolution. To show the difficulties in solving constrained optimization problems through GA, the 0/1 knapsack problem with user specific object preferences has been taken up. A new genetic operator, namely, ‘gene silencing’ inspired from biology is used along with standard GA. The experimental results for va...

Gene clustering is a familiar step in the exploratory analysis of high dimensional biological data. It is the process of grouping genes of similar patterns in the same cluster and aims at analyzing the functions of gene that leads to the development of drugs and early diagnosis of diseases. In the recent years, much research has been proposed using nature inspired meta-heuristic algorithms. Cuc...

BACKGROUND Gene Regulatory Networks (GRNs) have become a major focus of interest in recent years. Elucidating the architecture and dynamics of large scale gene regulatory networks is an important goal in systems biology. The knowledge of the gene regulatory networks further gives insights about gene regulatory pathways. This information leads to many potential applications in medicine and molec...

Gene rearrangements have been used successfully in phylogenetic reconstruction and comparative genomics, but usually under the assumption that all genomes have the same gene content and that no gene is duplicated. While these assumptions allow one to work with organellar genomes, they are too restrictive for nuclear genomes. The main challenge in handling more realistic data is how to deal with...