About this paper This paper illustrates how a SAS team of modelers used SAS Enterprise Miner and 2009 KDD Cup competition data to create a highly accurate model for predicting churn. They applied several data preparation, feature creation and dimension reduction techniques to prepare the data for modeling. They then used several machine learning approaches, including an open source model that could be incorporated into SAS Enterprise Miner. The models were accessed using the assigned validation criteria. Learn how they approached the problem and which model was declared the “winner.”.
Title: NCBI Data Mining Tools: NCBI Genome Workbench 1 NCBI Data Mining Tools NCBI Genome Workbench. http//www.ncbi.nlm.nih.gov/projects/gbench/ June 10, 2008 Ceske Budejovice, Czech Republic 2 Tools for Data Mining at NCBI Nucleotide Sequence Analysis Structures Protein Sequence Analysis and Proteomics Genome Analysis Gene Expression http//www.ncbi.nlm.nih.gov/Tools/ 3 Tools for Data Mining at NCBI Nucleotide Sequence Analysis Structures Protein Sequence Analysis and Proteomics Genome Analysis Gene Expression http//www.ncbi.nlm.nih.gov/Tools/ 4 Genome WorkbencH. What it is?. Provide an interactive, client side GUI. Provide a suite of annotation tools. Provide a platform for visualization and analysis. Provide an easily extensible platform.
View data from publicly available sequence databases, e.g. NCBI and your own private data. Display sequence data as. graphical sequence views, various alignment views, phylogenetic tree views, and tabular views of data. Align your private data to data in public databases, display your data in the context of public data, and retrieve BLAST results. 5 Genome Workbench Home Page http//www.ncbi.nlm.nih.gov/projects/gbench/ 6 Genome Workbench All Screens 7 Workspace vs Project. Projects Hold Data.
Workspaces Hold Projects. It is best to combine data that go together inside projects, and to use workspaces to hold collections of projects that may or may not go together. 8 Project Tree. The Project Tree is a view on your workspace and projects. The project tree shows you a hierarchical expansion of your data, and allows you to group data items into folders.
The Project Tree is available from the main menu at View -gt Project Tree. It is on by default, and appears on the left-hand side. 9 Tools Selection Inspector. The selection inspector provides a means for evaluating all the selected objects in Genome Workbench. The selection inspector has three modes of operation (Table, Brief Text, and Full Text), selectable by using the icons in the right-hand corner of the view. Aggregating selection across views.
Show selection from all view 10 Data Mining View. The data mining view is a view that combines many modes of searching into one interface. From the data mining view, you can search for items in the public sequence repository you can search for gene records from Entrez Gene you can search for annotations in a given view and you can search for patterns of sequences. The data mining view is on by default, and is available from View -gt Data Mining View. PowerShow.com is a leading presentation/slideshow sharing website. Whether your application is business, how-to, education, medicine, school, church, sales, marketing, online training or just for fun, PowerShow.com is a great resource.
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Presentations for free. Or use it to find and download high-quality how-to PowerPoint ppt presentations with illustrated or animated slides that will teach you how to do something new, also for free.
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Sas Data Mining Tools
For comprehensive understanding of precise morphological changes resulting from loss-of-function mutagenesis, a large collection of 1,899,247 cell images was assembled from 91,71 micrographs of 4782 budding yeast disruptants of non-lethal genes. All the cell images were processed computationally to measure approximately 500 morphological parameters in individual mutants. We have recently made this morphological quantitative data available to the public through the Saccharomyces cerevisiae Morphological Database (SCMD). Inspecting the significance of morphological discrepancies between the wild type and the mutants is expected to provide clues to uncover genes that are relevant to the biological processes producing a particular morphology. To facilitate such intensive data mining, a suite of new software tools for visualizing parameter value distributions was developed to present mutants with significant changes in easily understandable forms. In addition, for a given group of mutants associated with a particular function, the system automatically identifies a combination of multiple morphological parameters that discriminates a mutant group from others significantly, thereby characterizing the function effectively. These data mining functions are available through the World Wide Web at http://scmd.gi.k.u-tokyo.ac.jp/.
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact. For comprehensive understanding of precise morphological changes resulting from loss-of-function mutagenesis, a large collection of 1 899 247 cell images was assembled from 91 271 micrographs of 4782 budding yeast disruptants of non-lethal genes. All the cell images were processed computationally to measure 500 morphological parameters in individual mutants. Datel wifi max drivers download.
We have recently made this morphological quantitative data available to the public through the Saccharomyces cerevisiae Morphological Database (SCMD). Inspecting the significance of morphological discrepancies between the wild type and the mutants is expected to provide clues to uncover genes that are relevant to the biological processes producing a particular morphology. To facilitate such intensive data mining, a suite of new software tools for visualizing parameter value distributions was developed to present mutants with significant changes in easily understandable forms. In addition, for a given group of mutants associated with a particular function, the system automatically identifies a combination of multiple morphological parameters that discriminates a mutant group from others significantly, thereby characterizing the function effectively. These data mining functions are available through the World Wide Web. MORPHOLOGICAL DATABASE To study the global regulation of cell morphological characteristics, a number of groups have recently reported genome-wide screening data for yeast mutants with abnormal morphology (–).
Despite the relatively simple ellipsoidal shape of yeast cells, in the past, cell morphology researchers processed information on cells manually. These time consuming, entirely subjective tasks motivated us to develop image-processing software called CalMorph , which automatically extracts yeast cells from micrographs and processes them to measure morphological characteristics such as cell size, roundness, bud neck position angle, nuclear position and actin localization.
Using our software, we have retrieved 1 899 247 cells from 91 271 micrographs of 4782 mutants, which cover almost all of the yeast non-essential mutants cultured from the deleted strains available from EUROSCARF. All cell images, micrographs and quantitative values of morphological parameters are freely available from the SCMD database , which presents information that is complementary to the existing sequence and gene-expression databases (–). Workflow of image processing and data mining. ( A) Input photos of cells strained with FITC–ConA, DAPI and Rh-ph to visualize the cell wall, nuclei and actin distribution, respectively. ( B) Superimposition of three micrographs for individual cells. Displays image-processing results.
Our image-processing software first identifies the cell wall, attempts to fit an ellipse to each mother cell or bud and colors the cell wall green. The yellow lines show the long and short axes of the fitted ellipses. Bud necks that separate mother cells and buds are illustrated by using two red bullets. Identifying the cell wall makes it easier to determine information on the localization of nuclei and actin patches relative to the cell wall. In, nuclei and actin patches are represented using yellow and light blue bullets, respectively. Shows the primary morphological parameters of cells.
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The quantitative values of these parameters may change slightly from cell to cell. To perform rigorous statistical analysis of the significance of morphological changes, we need to know the distribution of morphological parameter values for individual cells; this requires that we collect an ample number of image-processed cells and their parameter values. More than 200 image-processed cells were collected for each mutant using a sufficient number of micrographs. Then, 500 morphological parameters were calculated for the mutants.
Search Morphological data should be useful for identifying the morphological changes in particular mutants. Users can query a yeast mutant of interest using its open reading frame name or its gene name. They can also browse average shapes of the mutant, average morphological parameter values, raw and processed micrographs and lists of individual cells associated with morphological parameter values. Users can also provide a typical morphological shape or a particular mutant as a query and ask the system to search for mutants that are similar in shape to the query. This function is called ‘morphology search’. Teardrop view—juxtaposition of morphological parameter distributions In order to understand which morphological parameters of a particular mutant are abnormal, the system displays the distribution of all mutants for each parameter and highlights the focal mutant value in pink (see ). The system juxtaposes the distributions of all parameters in parallel, making it easy for users to comprehend the overview of distributions and abnormal parameters at a glance.
Parameters are colored blue or pink if their changes are statistically significant in terms of their distributions. Mutant classification in terms of morphological parameters Another promising application of morphological parameters is to use them to predict gene functions. For instance, suppose that one is interested in finding a group of genes involved in a particular biological process such as DNA repairs and cell wall construction. You can ask the system to look for a combination of multiple morphological parameters that discriminate disruptants of genes that are known to be relevant to the biological process of interest (see ). These morphological parameters allow us to define distances between disruptants. If we identify disruptants that are not known to be related to any particular biological process but are closer to disruptants that are relevant to the focal biological process, these disrupted genes are potentially involved in the biological process.
Data Mining Tools Software
CUSTOMIZATION AND DATA AVAILABILITY To facilitate customization according to users' interests for the ease of browsing, a dialog-based interface for the parameter selection page helps users choose parameters displayed in datasheets and are memorized in the system. The system also allows users to download the list of selected parameter values for selected mutants in the XML format or in tabular form. Users can also select particular mutants of interest so that they are always shown in Teardrop View and 2D plot.