OpenLAB / RepTB

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Commit 9e86b051b4168f8e45f99c3f4c54a45e0ede70f3

Authored by anurag
1 parent d1208fbb7c
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Updated README and Python Code

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1 1 ###### Decription of the files on GitHub ######
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3   -1) RepTB_NBI_algorithm_snippet.ipynb : This python notebook contains only the python code for the NBI algorithm. It takes the Adjacent Matrix (A) created from the file new_dt_from_go_and_db_unique_latest.csv
  3 +1) nbi_simulation_new_for_git.ipynb : The NBI code snippet contains comments that would help the researchers with their own data. All the researchers have to do is upload their DTIs (as given in the new_dt_from_go_and_db_unique_latest.csv) and run the python code.
  4 + The code will first generate an adjacent matrix using the input file and then create a prediction matrix called NBIscore which will be a m x n matrix.
  5 + The output file (predicted_targets_for_all_drugs_using_percent_diff_0.20_new.csv) will be created which will contain predicted DTIs having scores within 20% of the max score for each Target.
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5   -2) new_dt_from_go_and_db_unique_latest.csv: This file contains the drug-target interactions from drugbank and GO mapping. Create an Adjacent matrix with this file and feed it into NBI.
  7 +2) new_dt_from_go_and_db_unique_latest.csv: This file contains the drug-target interactions from drugbank and GO mapping. This is the input file that we have used in our study.
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7 9 3) Generating_random_netoworks.m: This is a matlab file that was used to generate random networks with the same number of nodes but varying drug interactions and running through NBI.
  10 + This file was only used to establish the robustness of the NBI.
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9 12 4) uniprot_go_annotations_mf_strong_evidence_new.csv: This file contains the mapping of the molecular function GO terms and Uniprot Ids.
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11   -5) predicted_targets_for_all_drugs_using_percent_diff_0.20_new.csv: This is our prioritized list of predicted DTIs using NBI.
  14 +5) predicted_targets_for_all_drugs_using_percent_diff_0.20_new.csv: This is the final output file generated using the NBI python code and contains the prioritized list of predicted DTIs with the predicted scores.
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