ALBD

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README  view on Meta::CPAN

    of a co-occurrence matrix

    metaAnalysis.pl -- determines the number of rows, columns, vocabulary
    size, and total number of co-occurrences of a co-occurrence file, or set
    of co-occurrence files

    There is another folder containing scripts to square co-occurrence
    matrices. Squaring an explicit (A to B) co-occurrence matrix results in
    a co-occurrence matrix containing all implicit (A to C) connections.
    This is useful for time slicing and other analysis. Removal of the
    original explicit matrix is an additional step that is required if you
    wish to create a predictions matrix file for every CUI. This can be done
    with the removeExplicit.pl script. Squaring a co-occurrence matrix can
    be very computationally expensive, both in terms of ram and cpu. For
    this reason MATLAB scripts are preferred over perl scripts. Even using
    MATLAB ram can become an issue, and squaring sections of a matrix and
    combining them into a single output matrix may be necassary, but takes
    much longer. Scripts in the squaring folder include:

    convertForSquaring_MATLAB.pl -- functions to convert to and from ALBD
    and MATLAB sparse matrix formats

lib/LiteratureBasedDiscovery/Discovery.pm  view on Meta::CPAN


######################################################################
#                          Description
######################################################################
# Discovery.pm - provides matrix operations from  n11 counts from 
# UMLS::Association
#
#TODO I think some of these notes should be elsewhere
# 'B' term filtering may be applied by removing elements from the 
# explicit knowledge matrix before squaring. It is important to 
# replicate the original matrix before filtering so that explicit 
# knowledge can be removed from the implicit matrix.
# 'C' term filtering may be applied directly to the implicit
# knowledge matrix.
#
# A Typical workflow may look like:
# 1) load explicit knowledge from UMLS::Association
# 2) clone explicit knowledge (for removal from implicit)
# 3) apply filtering to explicit knowledge
# 4) square explicit knowledge to generate implicit knowledge
# 5) remove explicit knowledge from implicit knowledge



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