Datasets:

powidla
/

Friend-Or-Foe

annotations_creators:
  - synthetic
language_creators:
  - found
language:
  - en
multilinguality:
  - monolingual
license: apache-2.0
task_categories:
  - tabular-classification
  - tabular-regression
tags:
  - biology
  - synthetic
pretty_name: FOF
size_categories:
  - 10M<n<100M
dataset_type: tabular
configs:
  - config_name: Classification_AGORA_BC-I-100
    data_files:
      - split: train
        path: Classification/AGORA/100/BC-I/X_train_BC-I-100.csv
      - split: val
        path: Classification/AGORA/100/BC-I/X_val_BC-I-100.csv
      - split: test
        path: Classification/AGORA/100/BC-I/X_test_BC-I-100.csv
  - config_name: Classification_AGORA_BC-II-100
    data_files:
      - split: train
        path: Classification/AGORA/100/BC-II/X_train_BC-II-100.csv
      - split: val
        path: Classification/AGORA/100/BC-II/X_val_BC-II-100.csv
      - split: test
        path: Classification/AGORA/100/BC-II/X_test_BC-II-100.csv
  - config_name: Classification_AGORA_BC-III-100
    data_files:
      - split: train
        path: Classification/AGORA/100/BC-III/X_train_BC-III-100.csv
      - split: val
        path: Classification/AGORA/100/BC-III/X_val_BC-III-100.csv
      - split: test
        path: Classification/AGORA/100/BC-III/X_test_BC-III-100.csv
  - config_name: Classification_AGORA_BC-IV-100
    data_files:
      - split: train
        path: Classification/AGORA/100/BC-IV/X_train_BC-IV-100.csv
      - split: val
        path: Classification/AGORA/100/BC-IV/X_val_BC-IV-100.csv
      - split: test
        path: Classification/AGORA/100/BC-IV/X_test_BC-IV-100.csv
  - config_name: Classification_AGORA_BC-V-100
    data_files:
      - split: train
        path: Classification/AGORA/100/BC-V/X_train_BC-V-100.csv
      - split: val
        path: Classification/AGORA/100/BC-V/X_val_BC-V-100.csv
      - split: test
        path: Classification/AGORA/100/BC-V/X_test_BC-V-100.csv
  - config_name: Classification_AGORA_MC-I-100
    data_files:
      - split: train
        path: Classification/AGORA/100/MC-I/X_train_MC-I-100.csv
      - split: val
        path: Classification/AGORA/100/MC-I/X_val_MC-I-100.csv
      - split: test
        path: Classification/AGORA/100/MC-I/X_test_MC-I-100.csv
  - config_name: Classification_AGORA_MC-II-100
    data_files:
      - split: train
        path: Classification/AGORA/100/MC-II/X_train_MC-II-100.csv
      - split: val
        path: Classification/AGORA/100/MC-II/X_val_MC-II-100.csv
      - split: test
        path: Classification/AGORA/100/MC-II/X_test_MC-II-100.csv
  - config_name: Classification_AGORA_MC-III-100
    data_files:
      - split: train
        path: Classification/AGORA/100/MC-III/X_train_MC-III-100.csv
      - split: val
        path: Classification/AGORA/100/MC-III/X_val_MC-III-100.csv
      - split: test
        path: Classification/AGORA/100/MC-III/X_test_MC-III-100.csv
  - config_name: Classification_AGORA_BC-I-50
    data_files:
      - split: train
        path: Classification/AGORA/50/BC-I/X_train_BC-I-50.csv
      - split: val
        path: Classification/AGORA/50/BC-I/X_val_BC-I-50.csv
      - split: test
        path: Classification/AGORA/50/BC-I/X_test_BC-I-50.csv
  - config_name: Classification_AGORA_BC-II-50
    data_files:
      - split: train
        path: Classification/AGORA/50/BC-II/X_train_BC-II-50.csv
      - split: val
        path: Classification/AGORA/50/BC-II/X_val_BC-II-50.csv
      - split: test
        path: Classification/AGORA/50/BC-II/X_test_BC-II-50.csv
  - config_name: Classification_AGORA_BC-III-50
    data_files:
      - split: train
        path: Classification/AGORA/50/BC-III/X_train_BC-III-50.csv
      - split: val
        path: Classification/AGORA/50/BC-III/X_val_BC-III-50.csv
      - split: test
        path: Classification/AGORA/50/BC-III/X_test_BC-III-50.csv
  - config_name: Classification_AGORA_BC-IV-50
    data_files:
      - split: train
        path: Classification/AGORA/50/BC-IV/X_train_BC-IV-50.csv
      - split: val
        path: Classification/AGORA/50/BC-IV/X_val_BC-IV-50.csv
      - split: test
        path: Classification/AGORA/50/BC-IV/X_test_BC-IV-50.csv
  - config_name: Classification_AGORA_BC-V-50
    data_files:
      - split: train
        path: Classification/AGORA/50/BC-V/X_train_BC-V-50.csv
      - split: val
        path: Classification/AGORA/50/BC-V/X_val_BC-V-50.csv
      - split: test
        path: Classification/AGORA/50/BC-V/X_test_BC-V-50.csv
  - config_name: Classification_AGORA_MC-I-50
    data_files:
      - split: train
        path: Classification/AGORA/50/MC-I/X_train_MC-I-50.csv
      - split: val
        path: Classification/AGORA/50/MC-I/X_val_MC-I-50.csv
      - split: test
        path: Classification/AGORA/50/MC-I/X_test_MC-I-50.csv
  - config_name: Classification_AGORA_MC-II-50
    data_files:
      - split: train
        path: Classification/AGORA/50/MC-II/X_train_MC-II-50.csv
      - split: val
        path: Classification/AGORA/50/MC-II/X_val_MC-II-50.csv
      - split: test
        path: Classification/AGORA/50/MC-II/X_test_MC-II-50.csv
  - config_name: Classification_AGORA_MC-III-50
    data_files:
      - split: train
        path: Classification/AGORA/50/MC-III/X_train_MC-III-50.csv
      - split: val
        path: Classification/AGORA/50/MC-III/X_val_MC-III-50.csv
      - split: test
        path: Classification/AGORA/50/MC-III/X_test_MC-III-50.csv

Friend or Foe

Welcome to the Friend or Foe Collection!

This file contains the description and general structure of the Friend or Foe v.1 collection. The tree-like structure of this folder can be found in tree.txt.

This folder has 5 subfolders related to different ML tasks, namely Classification, Regression, Transfer Learning, Generative Modeling, and Clustering. Each subfolder stores datasets that address a specific task of evaluating the microbial interactions suitable for the machine learning pipeline. All datasets are stored as large sparse tables in .csv format with corresponding indices for the chemical compounds (features) marked as "C_j", where j is in range(1,424) for AGORA and in range(1,499) for CARVEME. In addition, pre-processed values of the environmental matrices with the provided train/val/test split are stored as .npy files to simplify the ML pipeline.

In addition, we provide full mapping of indices to real names of compounds in compounds.json. Note that for the clustering datasets, we changed the indices as we selected specific compounds, so the provided compounds.json is different from the one for supervised learning, transfer learning, and generative modeling. For clustering, we also provide names of species used in a specific dataset as species.json. Species used for other tasks can be found in the utils folder.

The utils subfolder contains all the metabolic models that we used for the friend or foe construction. All metabolic models are stored in .mat format, the number also identifies the species. In addition, we provide the environment results of the metabolic models in raw .csv files that collect the environments for specific interactions. A General info subfolder contains information about organism names and abbreviations of environmental compounds (to convert the abbreviation to a name, see Agora_env_compounds.xlsx for AGORA and the Bigg database at http://bigg.ucsd.edu for CARVEME).

Citation Information

TBD