OpenLAB / RepTB

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Commit 9e86b051b4168f8e45f99c3f4c54a45e0ede70f3

Authored by anurag
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Updated README and Python Code

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1 ###### Decription of the files on GitHub ###### 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 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. 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 4) uniprot_go_annotations_mf_strong_evidence_new.csv: This file contains the mapping of the molecular function GO terms and Uniprot Ids. 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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