Browsing by Subject "Microarray Data"

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    Inferring gene regulatory networks from asynchronous microarray data with AIRnet
    (BMC, 2010-11-02) Oviatt, David; Clement, Mark; Snell, Quinn; Sundberg, Kenneth; Lai, Chun Wan J.; Allen, Jared; Roper, Randall J.; Biology, School of Science
    Background 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 or no time series guarantees. When several samples are averaged to examine differences in mean value between a diseased and normal state, information from individual samples that could indicate a gene relationship can be lost. Results Asynchronous Inference of Regulatory Networks (AIRnet) provides gene signaling network inference using more practical assumptions about the microarray data. By learning correlation patterns for the changes in microarray values from all pairs of samples, accurate network reconstructions can be performed with data that is normally available in microarray experiments. Conclusions By focussing on the changes between microarray samples, instead of absolute values, increased information can be gleaned from expression data.
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    Sibios as a Framework for Biomarker Discovery Using Microarray Data
    (2006-07-26T18:57:33Z) Choudhury, Bhavna; Mahoui, Malika
    Decoding the human genome resulted in generating large amount of data that need to be analyzed and given a biological meaning. The field of Life Schiences is highly information driven. The genomic data are mainly the gene expression data that are obtained from measurement of mRNA levels in an organism. Efficiently processing large amount of gene expression data has been possible with the help of high throughput technology. Research studies working on microarray data has led to the possibility of finding disease biomarkers. Carrying out biomarker discovery experiments has been greatly facilitated with the emergence of various analytical and visualization tools as well as annotation databases. These tools and databases are often termed as 'bioinformatics services'. The main purpose of this research was to develop SIBIOS (Bystem for Integration of Bioinformatics Services) as a platform to carry out microarray experiments for the purpose of biomarker discovery. Such experiments require the understanding of the current procedures adopted by researchers to extract biologically significant genes. In the course of this study, sample protocols were built for the purpose of biomarker discovery. A case study on the BCR-ABL subtype of ALL was selected to validate the results. Different approaches for biomarker discovery were explored and both statistical and mining techniques were considered. Biological annotation of the results was also carried out. The final task was to incorporate the new proposed sample protocols into SIBIOS by providing the workflow capabilities and therefore enhancing the system's characteristics to be able to support biomarker discovery workflows.