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    IBM Genes@Work

    Category  Genomics>Gene Expression Analysis/Profiling/Tools

    Abstract  IBM Genes@Work is a pattern discovery and classification
    system for gene expression data. It was designed to provide a rich set
    of tools for the analysis of gene expression data. Unique to
    Genes@Work is the search of "patterns" over a particular set of DNA
    microarray samples. A pattern is a collection of genes with similar
    expression levels over a subset of experiments. Each microarray is
    known a priori to belong to either one of two "phenotypes" under study.
    One of these phenotypes is used as a "control" phenotype, and the
    other phenotype will be termed the "target". The patterns are searched
    over the target phenotype. Patterns can be further applied to selecting
    relevant genes and building predictive models. Genes@Work provides
    an integrated set of tools for exploring and building predictive model
    hypothesis by using supervised learning techniques such as Support
    Vector Machines (SVM). Genes@Work features include:

    1) Two types of input data - data can be formatted in either one of two
    tab separated formats (Affymetrix or Complimentary DNA). And, two
    different files are required for processing, one with the gene expression
    data and another describing the target phenotypes.

    2) Visualizing gene array data - gene expression data can be visualized
    through a variety of color plots, scatter plots, pattern plots and
    hierarchical clustering dendrograms.

    3) Advanced data preprocessing features include extensive scaling,
    filtering and feature selection. Expression data may be pre-processed
    before running the pattern discovery portion of the system. The pre-
    processing may enhance the ability to find a signal in the data. For
    example, filtering could be used to ignore genes that are Not present
    as judged by the Affymetrix call. Alternatively, filtering could be used to
    ignore genes that show insignificant change in expression across
    samples.

    Additionally, data can be scaled by taking logarithms above a given
    threshold. Scaling options include: Apply cutoff threshold, Apply loge
    transformation and Apply normalization/scaling to average. Filtering
    options include: % Present Calls less than, Standard deviation, by
    threshold; Standard deviation, by number of genes; Import feature list
    from file, etc.

    Feature selection options include: Fold ratio, by number of genes; Fold
    ratio, by threshold; USE-Fold filter for Affymetrix data,
    SNR (Signal to Noise Ratio), by number of genes; SNR, by threshold;
    etc.

    4) Advanced Pattern Discovery parameters include: a) Phenotype -
    indicates the group of microarray samples over which patterns are
    searched, b) Delta - indicates the maximum deviation in normalized
    expression units for a gene to be included in a pattern, c) Min Support -
    indicates a search strategy for patterns, d) Max Pattern Count -  
    indicates a search strategy for patterns alternative to Min Support, e)
    Threshold - sets whether a pattern is reported in relation to how often
    such a pattern should occur by random chance, f) Genes - indicates the
    minimum number of genes the reported patterns must contain, g)
    Independent Patterns - when this option is enabled, the program lists
    only the maximal patterns that are Not conditionally dependent with
    each other, etc.

    5) Genes@Work supports hierarchical clustering for use in
    combination with pattern discovery or as an independent tool.

    6) Genes@Work advanced graphical Classification sub-system allows
    you to classify based on patterns, generate predictive hypothesis and
    apply them to previously unseen data. Its Learning Machine options
    includes the following learning algorithms - Support Vector Machines
    (SVM), k-Nearest Neighbors (k-NN), Pattern Discovery based
    classification (PD) and an experimental hybrid between PD and SVM
    (PD/SVM), and more.

    System Requirements  PC environment; runs from DOS shell

    Manufacturer   Home office; see web site for international locations.

    IBM Research
    P.O. Box 218,
    Yorktown Heights, N.Y. 10598, USA
    IBM Corporation
    1 New Orchard Road
    Armonk, New York 10504-1722
    1-800-IBM-4YOU
    (1-800-426-4968)
    Technical Support: 1-800-IBM-SERV
    (1-800-426-7378)

    Manufacturer's Web Site  www.research.ibm.
    com/FunGen/FGDownloads.htm

    Price  Contact manufacturer

    G6G Product Number  20004

    G6G Manufacturer Number  101280
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