ALBD

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CHANGELOG  view on Meta::CPAN

NAME
    ALBD CHANGES

  Changes from version 0.00 to 0.01
    First Release, leaving here to show how change log should be updated

  Changes from version 0.01 to 0.03
    CPAN problems, so rereleased. No major changes

  Changes from version 0.01 to 0.05
    CPAN problems, so changed LiteratureBasedDiscovery.pm to ALBD.pm

FDL.txt  view on Meta::CPAN

a complete Transparent copy of the Document, free of added material.
If you use the latter option, you must take reasonably prudent steps,
when you begin distribution of Opaque copies in quantity, to ensure
that this Transparent copy will remain thus accessible at the stated
location until at least one year after the last time you distribute an
Opaque copy (directly or through your agents or retailers) of that
edition to the public.

It is requested, but not required, that you contact the authors of the
Document well before redistributing any large number of copies, to give
them a chance to provide you with an updated version of the Document.


4. MODIFICATIONS

You may copy and distribute a Modified Version of the Document under
the conditions of sections 2 and 3 above, provided that you release
the Modified Version under precisely this License, with the Modified
Version filling the role of the Document, thus licensing distribution
and modification of the Modified Version to whoever possesses a copy
of it.  In addition, you must do these things in the Modified Version:

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

use warnings;

use UMLS::Interface;

# applies a semantic group filter to the matrix, by removing keys that 
# are not allowed semantic type. Eliminates both rows and columns, so
# is applied to the full explicit matrix
# input:  $matrixRef <- ref to a sparse matrix to be filtered
#         $acceptTypesRef <- a ref to a hash of accept type strings
#         $umls <- an instance of UMLS::Interface
# output: None, but $vectorRef is updated 
sub semanticTypeFilter_rowsAndColumns {
    my $matrixRef = shift;
    my $acceptTypesRef = shift;
    my $umls = shift;
 
=comment   
    #Count the number of keys before and after filtering (for debugging)
    my %termsHash = ();
    foreach my $key1 (keys %{$matrixRef}) {
	foreach my $key2 (keys %{${$matrixRef}{$key1}}) {

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

}


# applies a semantic group filter to the matrix, by removing keys that 
# are not allowed semantic type. Only removes types from rows, 
# so is applied for times slicing, before randomly selecting terms of 
# one semantic type
# input:  $matrixRef <- ref to a sparse matrix to be filtered
#         $acceptTypesRef <- a ref to a hash of accept type strings
#         $umls <- an instance of UMLS::Interface
# output: None, but $vectorRef is updated 
sub semanticTypeFilter_rows {
    my $matrixRef = shift;
    my $acceptTypesRef = shift;
    my $umls = shift;
    
=comment
    #Count the number of keys before and after filtering (for debugging)
    my %termsHash = ();
    foreach my $key1 (keys %{$matrixRef}) {
	foreach my $key2 (keys %{${$matrixRef}{$key1}}) {

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

}


# applies a semantic group filter to the matrix, by removing keys that 
# are not allowed semantic type. Only removes types from columns, 
# so is applied to the implicit matrix (starting term rows with implicit
# columns).
# input:  $matrixRef <- ref to a sparse matrix to be filtered
#         $acceptTypesRef <- a ref to a hash of accept type strings
#         $umls <- an instance of UMLS::Interface
# output: None, but $vectorRef is updated 
sub semanticTypeFilter_columns {
    my $matrixRef = shift;
    my $acceptTypesRef = shift;
    my $umls = shift;
 
=comment   
    #Count the number of keys before and after filtering (for debugging)
    my %termsHash = ();
    foreach my $key1 (keys %{$matrixRef}) {
	foreach my $key2 (keys %{${$matrixRef}{$key1}}) {

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

#XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
#XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

# gets association scores for a set of cui pairs 
# input:  $cuiPairsRef <- reference to a hash of pairs of matrix indeces (key = '1,2')
#         $matrixRef <- a reference to a sparse matrix of n11 values
#         $measure <- the association measure to perform
#         $association <- an instance of UMLS::Association
# output: none, bu the cuiPairs ref has values updated to reflect the 
#         computed assocation score
sub getBatchAssociationScores {
    my $cuiPairsRef = shift;
    my $matrixRef = shift;
    my $measure = shift;
    my $association = shift;
    
    #optionally pass in $n1pRef, $np1Ref, and $npp
    # do this if they get calculated multiple times
    # (such as with time slicing)

samples/lbdConfig  view on Meta::CPAN

#   linkingTermCount* (LTC) - count of shared linking terms
#   frequency* (freq) - sum of A to B co-occurrences of shared B terms
#   ltcAssociation (LTA) - association measures that use linking terms as inputs
#   ltc_AMW - linking term count with AMW as a tie-breaker
#
#   *all procedures require a measure to be specified except LTC and freq
<rankingProcedure>averageMinimumWeight

# The association measure to use as a value in the ranking procedure.
# The string is passed directly to UMLS::Association, so as that gets
# updated, new associatio measures will work automatically.
# At the time of this writing, valid arguments are:
# freq - Frequency
# dice - Dice Coefficient
# left - Fishers exact test - left sided 
# right - Fishers exact test - right sided 
# twotailed - Fishers twotailed test
# jaccard - Jaccard Coefficient
# ll - Log-likelihood ratio
# tmi - Mutual Information
# odds - Odds Ratio

samples/timeSlicingConfig  view on Meta::CPAN

#   linkingTermCount* (LTC) - count of shared linking terms
#   frequency* (freq) - sum of A to B co-occurrences of shared B terms
#   ltcAssociation (LTA) - association measures that use linking terms as inputs
#   ltc_AMW - linking term count with AMW as a tie-breaker
#
#   *all procedures require a measure to be specified except LTC and freq
<rankingProcedure>averageMinimumWeight

# The association measure to use as a value in the ranking procedure.
# The string is passed directly to UMLS::Association, so as that gets
# updated, new associatio measures will work automatically.
# At the time of this writing, valid arguments are:
# freq - Frequency
# dice - Dice Coefficient
# left - Fishers exact test - left sided 
# right - Fishers exact test - right sided 
# twotailed - Fishers twotailed test
# jaccard - Jaccard Coefficient
# ll - Log-likelihood ratio
# tmi - Mutual Information
# odds - Odds Ratio