Biological Data Warehousing System for Identifying Transcriptional Regulatory Sites From Gene Expressions of Microarray Data

RgS-Miner: A Biological Data Warehousing, Analyzing and Mining System for Identifying Transcriptional Regulatory Sites in Human Genome

Recently, biological databases and analytical methods have become available for analyzing gene expression and transcriptional regulatory sequences. However, users must make the complicated analyses to query the data in various databases, and then they must analyze the gene upstreams using various predictive tools, before finally converting date among formats. Beyond methods for predicting trans...

Gene Identification from Microarray Data for Diagnosis of Acute Myeloid and Lymphoblastic Leukemia Using a Sparse Gene Selection Method

Background: Microarray experiments can simultaneously determine the expression of thousands of genes. Identification of potential genes from microarray data for diagnosis of cancer is important. This study aimed to identify genes for the diagnosis of acute myeloid and lymphoblastic leukemia using a sparse feature selection method. Materials and Methods: In this descriptive study, the expressio...

Identifying Gene Regulatory Networks from Gene Expression Data

University of California, Davis 27.

cDNA Microarray Data Normalization

Reverse engineering transcriptional regulatory networks from gene expression microarray data using qpgraph

This vignette describes how to use the package qpgraph in order to reverse engineer a transcriptional regulatory network from a particular gene expression microarray data set of Escherichia coli (E. coli). Concretely, the data corresponds to n = 43 experiments of various mutants under oxygen deprivation (Covert et al., 2004). The mutants were designed to monitor the response from E. coli during...