objective: despite the huge efforts, chronic kidney disease (ckd) remains as an unsolved problem in medicine. many studies have shown a central role for transforming growth factor beta-1 (tgfβ-1) and its downstream signaling cascades in the pathogenesis of ckd. in this study, we have reanalyzed a microarray dataset to recognize critical signaling pathways controlled by tgfβ-1. materials and met...

background: one substantial part of microarray studies is to predict patients’ survival based on their gene expression profile. variable selection techniques are powerful tools to handle high dimensionality in analysis of microarray data. however, these techniques have not been investigated in competing risks setting. this study aimed to investigate the performance of four sparse variable selec...

A challenging task in time series microarray data analysis is to identify co-expressed groups of genes from a large input space. The overall objective of this study is to obtain knowledge about the most important genes and clusters related to production and growth rate in a real-world microarray data analysis task. Various measures are engaged to evaluate the importance of each gene and to grou...

dna microarray is a powerful approach to study simultaneously, the expression of 1000 of genes in a single experiment. the average value of the fluorescent intensity could be calculated in a microarray experiment. the calculated intensity values are very close in amount to the levels of expression of a particular gene. however, determining the appropriate position of every spot in microarray im...

Identifying genes underlying complex diseases/traits that generally involve multiple etiological mechanisms and contributing genes is difficult. Although microarray technology has enabled researchers to investigate gene expression changes, but identifying pathobiologically relevant genes remains a challenge. To address this challenge, we apply a new method for selecting the disease-relevant gen...