From 9e86b051b4168f8e45f99c3f4c54a45e0ede70f3 Mon Sep 17 00:00:00 2001 From: anurag Date: Fri, 13 Apr 2018 15:56:14 +0530 Subject: [PATCH] Updated README and Python Code --- README | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/README b/README index d16a99b..030729a 100644 --- a/README +++ b/README @@ -1,12 +1,15 @@ ###### Decription of the files on GitHub ###### -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 +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. + 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. + 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. -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. +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. 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. + This file was only used to establish the robustness of the NBI. 4) uniprot_go_annotations_mf_strong_evidence_new.csv: This file contains the mapping of the molecular function GO terms and Uniprot Ids. -5) predicted_targets_for_all_drugs_using_percent_diff_0.20_new.csv: This is our prioritized list of predicted DTIs using NBI. +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. -- 2.0.0