Microarray contains a large matrix of information and has been widely used by biologists bio data scientist for monitoring combinations genes in different organisms. The coherent patterns all continuous columns are mined gene microarray matrices. It is investigated, this study, the developing time series similarity measure columns, as well evaluation function verifying proposed algorithm corres...

Characterizing the genome-wide dynamic regulation of gene expression is important and will be of much interest in the future. However, there is currently no established method for identifying differentially expressed genes in a time course study. Here we propose a significance method for analyzing time course microarray studies that can be applied to the typical types of comparisons and samplin...

Data mining of microarray data more often includes exploration of temporal data. E.g., researchers might look for temporal changes in gene expression to determine responses to an injury. Knowledge-based Temporal Abstraction does not assume that the pattern of time points is similar to the original experimental design. We extended this framework by creating an software tool using open-source pla...

Microarray technology is beneficial in terms of diagnosing various diseases, including cancer. Despite all DNA microarray benefits, the high number genes versus low samples has always been a crucial challenge for this technology. Accordingly, we need new optimization algorithms to select optimal faster disease diagnosis. In article, version binary cat algorithm, named SBCSO, gene selection expr...

dna microarrays consist of collection of dna microscopic spots that in order to form an array attached to a solid surface such as glass, plastic or silicon chip. the pieces of fixed dna considered as a searcher. in this technology it is possible to test sample against thousands probes for specific genes. with this ability, arrays accelerate the biological investigations, gene finding, molecular...

Microarray technology provides an opportunity for scientists to analyze thousands of gene expression profiles simultaneously. However, microarray gene expression data often contain multiple missing expression values due to many reasons. Effective methods for missing value imputation in gene expression data are needed since many algorithms for gene analysis require a complete matrix of gene arra...

Modern approaches to treating genetic disorders, cancers and even epidemics rely on a detailed understanding of the underlying gene signaling network. Previous work has used time series microarray data to infer gene signaling networks given a large number of accurate time series samples. Microarray data available for many biological experiments is limited to a small number of arrays with little...