Bioinformatics_and_Other_DM_Applications

Bioinformatics_and_Other_DM_Applications - Bioinformatics...

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1 Bioinformatics and Other DM Applications Kwok-Leung Tsui H. Milton Stewart School of Industrial & Systems Engineering, Georgia Tech
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2 Outline z Bioinformatics and Microarray Experiments z Experiment and Data Preparation z Data Mining and Statistical Modeling z Class Comparison z Class Prediction – Classification z Correlation and Sampling z Experimental Design and Split-plot Experiments z Other DM Applications
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3 Bioinformatics z A multi-disciplinary research field z Need knowledge from biology, statistics, and computer science z Research topics include: Source: http://www.dbmi.columbia.edu/bioinformatics/docs/ResearchOpportunities.html
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4 Data Mining and Statistical Methods for Analyzing Microarray Experiments Kwok-Leung Tsui Shin-Lian Lo H. Milton Stewart School of Industrial & Systems Engineering, Georgia Tech Benjamin Barwick Center of Medical Genomics Winship Cancer Institute, Emory University ISBRA (5/9/2008)
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5 DNA Microarray Experiments z DNA microarray is a collection of microscopic DNA spots arrayed on a solid surface by covalent attachment to a chemical matrix. (Wikipedia) z DNA microarrays are commonly used for expression profiling, i.e., monitoring expression levels of thousands of genes simultaneously , or for comparative genomic hybridization. (Wikipedia)
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6 Experiment Summary z Extract tissue samples from breast cancer patients and place them on two Sentrix Array Matrix (SAM) panels (plates). z Each panel goes through cDNA Annealing Selection extension and Ligation (DASL) experiment and contains 12x8 samples. z Image fluorescent intensities are interpreted and exported by BeadStudio. z Remove controls and failed samples z Failed samples are defined as (1) the average signal intensity < 3000 or (2) the background and noise signal > 2000. Æ 151 cleaned samples.
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7 Data Preparation z A microarray experiment led by Dr. Brian Leyland-Jones (M.D. Ph.D.), Executive Director, Winship Cancer Institute. z Extract tissue samples from breast cancer patients and place them on two Sentrix Array Matrix (SAM) panels (plates). z Each panel goes through cDNA Annealing Selection extension and Ligation (DASL) experiment and contains 12x8 samples. z Image fluorescent intensities are interpreted and exported by BeadStudio. z Remove controls and failed samples z Failed samples are defined as (1) the average signal intensity < 3000 or (2) the background and noise signal > 2000. Æ 151 cleaned samples.
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8 Breast Cancer Background z Breast cancer subtype traditionally defined by Immunohistochemistry (IHC) for: z ER – estrogen receptor z PR – progesterone receptor z HER2 – human epidermal growth factor receptor 2 z IHC determines if ER, PR, & HER2 proteins are present in cancerous tissue z This in turn may determine a treatment course z NNN (ER - PR - HER2 - ) is typically the most aggressive subtype and patients with this subtype have the worst prognosis
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9 Breast Cancer Background z When surgery is performed to remove a tumor it is typically either: z Mastectomy – breast removing surgery z Lumpectomy – breast conserving surgery z Tissue is saved in F
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This note was uploaded on 11/13/2010 for the course ISE 680 taught by Professor Santanu during the Spring '10 term at Purdue University Calumet.

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Bioinformatics_and_Other_DM_Applications - Bioinformatics...

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