license: apache-2.0
Data file description:
Two different scenarios were considered for prediction performance evaluation: sample-level holdout validation and entity-level holdout validation.
Sample-level holdout validation:
This scenario was used for evaluating model performance based on sample-level holdout validation. In this scenario, all the input samples in the test set were never seen during training. One sample corresponds to one label.
Data files include:
"drug-induced_gene_expression_change_sample-level_holdout_test_set.csv"
"drug-protein_binding_sample-level_holdout_test_set.csv"
"TF-gene_association_sample-level_holdout_test_set.csv"
"drug_sensitivity_sample-level_holdout_test_set.csv"
"gene_effect_score_sample-level_holdout_test_set.csv"
"gene_mutation_sample-level_holdout_test_set.csv"
"CNV_sample-level_holdout_test_set.csv"
Entity-level holdout validation:
This scenario was used for evaluating model prediction performance on input samples containing unseen entities, such as unseen cell lines and unseen compounds, which did not appear in the training set and were viewed by the model as new cell lines and new compounds in the test set to predict on.
Data files include:
"drug-induced_gene_expression_change_entity-level_holdout_test_set.csv"
"drug-protein_binding_entity-level_holdout_test_set.csv"
"TF-gene_association_entity-level_holdout_test_set.csv"
"drug_sensitivity_entity-level_holdout_test_set.csv"
"gene_effect_score_entity-level_holdout_test_set.csv"
"gene_mutation_entity-level_holdout_test_set.csv"
"CNV_entity-level_holdout_test_set.csv"
Model prediction performance is generally better under sample-level validation setting compared to the more stringent entity-level validation setting. We will release the training set after the acceptance of the paper.
Dataset field discription:
| Column | Description | Used by |
|---|---|---|
SMILES |
Drug structure in SMILES notation | Drug-induced expression, drug-protein binding, drug sensitivity |
cell_iname |
Cell line name | Drug-induced expression, drug sensitivity, gene effect, mutation, CNV |
gene_name |
Gene name | Drug-induced expression, TF-gene, gene effect, mutation, CNV |
target_sequence |
Protein amino acid sequence | Drug-protein binding, TF-gene |
time_h |
Treatment duration in hours | Drug-induced expression |
dose_uM |
Treatment concentration in μM | Drug-induced expression |
task_id |
Name of the task | All seven tasks |
label |
Ground truth label corresponding to each input sample (continuous values for regression tasks, binary values for classification tasks) | All seven tasks |
Downloading data:
Check our github repo on how to download the data, run InsilicoCell for prediction and evaluation.