Data mining of gene expression changes in Alzheimer brain

Abstract

Genome wide transcription profiling is a powerful technique for studying the enormous complexity of cellular states. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. The data requires care in both pre-processing and the application of data mining techniques. This paper addresses the problem of dealing with microarray data that come from two known classes (Alzheimer and normal). We have applied three separate techniques to discover genes associated with Alzheimer disease (AD). The 67 genes identified in this study included a total of 17 genes that are already known to be associated with Alzheimers or other neurological diseases. This is higher than any of the previously published Alzheimer's studies. Twenty known genes, not previously associated with the disease, have been identified as well as 30 uncharacterized Preprint submitted to Elsevier Science 3 June 2003Expressed Sequence Tags (ESTs). Given the success in identifying genes already associated with AD, we can have some confidence in the involvement of the latter genes and ESTs.From these studies we can attempt to define therapeutic strategies that would prevent the loss of specific components of neuronal function in susceptible patients or be in a position to stimulate the replacement of lost cellular function in damaged neurons.Although our study is based on a relatively small number of patients (4 AD and 5 normal), we think our approach sets the stage for a major step in using gene expression data for disease modelling (i.e. classification and diagnosis). It can also contribute to the future of gene function identification, pathology, toxicogenomics, and pharmacogenomics.

A unique gene expression signature discriminates familial Alzheimer's disease mutation carriers from their wild-type siblings

“…Different approaches have been used to elucidate the molecular and cellular malfunctions that contribute to the disease pathogenesis in AD (8)(9)(10)(11)(12)(13)(14)(15)(16)(17). Among the tissues studied are neuronal tissue, skin fibroblasts, platelets, lymphocytes, and body fluids, such as plasma and cerebrospinal fluid (18).…”

Interpreting fuzzy clustering results with virtual reality-based visual data mining: Application to microarray gene expression data

“…Alzheimer's disease (AD) is a chronic, progressive, debilitating condition which, along with other neuro-degenerative diseases, represents the largest area of unmet need in modern medicine [12], and there is now renewed hope that genomics technologies, particularly gene expression profiling, can contribute significantly to the understanding of the disease. Genome-wide expression profiling of thousands of genes provides rich datasets that can be mined to extract information on the genes that best characterize the disease.…”

Single cell gene expression profiling in Alzheimer’s disease

“…Regional analysis of gene expression is a widely used paradigm because of the relatively large amounts of RNA that can be extracted from carefully dissected frozen animal model tissues as well as postmortem human brain tissues, as evidenced by reports on amyloid overexpression 105,106 and several neurodegenerative disorders, including AD. [107][108][109][110][111][112][113][114][115][116] An advantage of regional gene expression analysis is that, in most cases, extracted RNA is sufficient to generate significant hybridization signal intensity for microarray analysis, enabling the analysis of thousands of targets without necessitating RNA amplification protocols. However, expression profiles garnered from regional dissections cannot discern molecular signatures in discrete neuronal populations nor can regional assessments evaluate differences in admixed neuronal and nonneuronal populations.…”