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Positive and Unlabeled Learning for Prioritization

Please cite our 2013 publication when using this work:

Wagner, A. H. et al. Prioritization of retinal disease genes: an integrative approach. Hum Mutat 34, 853-859, doi:10.1002/humu.22317 (2013).

Here is a compiled and packaged version of our code for our project. This runs the logistic regression method for producing probabilities for each gene. The output could then be redirected into a text file and then sorted in decreasing order of probabilities to get the ranking.

Feature vectors are for the training of a retinal degenerative disease model, as described in our paper. Classifier results are from training features.csv using PULP with the respective regression function.

Usage:

java -jar -Xmx2G CG_Method_LogisticRegression.jar features.csv

OR

java -jar -Xmx2G CG_Method_LogisticRegression.jar features.csv > output.csv

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Prioritization of Retinal Disease Genes: An Integrative Approach

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