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README
| 1 | 1 | ###### Decription of the files on GitHub ###### |
| 2 | 2 | |
| 3 | 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. | |
| 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. | |
| 6 | 6 | |
| 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. | |
| 7 | +2) new_dt_from_go_and_db_unique_latest.csv: This is the input file that we have used used in our study. This file contains the drug-target interactions from drugbank and GO mapping. | |
| 8 | 8 | |
| 9 | 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 | 10 | This file was only used to establish the robustness of the NBI. |
| 11 | 11 | |
| 12 | 12 | 4) uniprot_go_annotations_mf_strong_evidence_new.csv: This file contains the mapping of the molecular function GO terms and Uniprot Ids. |
| 13 | 13 | |
| 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. | |
| 14 | +5) predicted_targets_for_all_drugs_using_percent_diff_0.20_new.csv: This is the final output file generated using our input file and contains the prioritized list of predicted DTIs with the predicted scores. | |
| 15 | + If you are uploading your own input file containing the DTIs, you might need to change the name of the output file in the python code provided. | |
| 15 | 16 | ... | ... |