genedisco 1.0.5

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GeneDisco: A benchmark for active learning in drug discovery

In vitro cellular experimentation with genetic interventions, using for example CRISPR technologies, is an essential step in early-stage drug discovery and target validation that serves to assess initial hypotheses about causal associations between biological mechanisms and disease pathologies. With billions of potential hypotheses to test, the experimental design space for in vitro genetic experiments is extremely vast, and the available experimental capacity - even at the largest research institutions in the world - pales in relation to the size of this biological hypothesis space.

GeneDisco (published at ICLR-22) is a benchmark suite for evaluating active learning algorithms for experimental design in drug discovery. GeneDisco contains a curated set of multiple publicly available experimental data sets as well as open-source i mplementations of state-of-the-art active learning policies for experimental design and exploration.

GeneDisco ICLR-22 Challenge

Learn more about the GeneDisco challenge for experimental design for optimally exploring the vast genetic intervention space here.

Install

pip install genedisco

Use

How to Run the Full Benchmark Suite?

Experiments (all baselines, acquisition functions, input and target datasets, multiple seeds) included in GeneDisco can be executed sequentially for e.g. acquired batch size 64, 8 cycles and a bayesian_mlp model using:

run_experiments \
  --cache_directory=/path/to/genedisco_cache  \
  --output_directory=/path/to/genedisco_output  \
  --acquisition_batch_size=64  \
  --num_active_learning_cycles=8  \
  --max_num_jobs=1

Results are written to the folder at /path/to/genedisco_cache, and processed datasets will be cached at /path/to/genedisco_cache (please replace both with your desired paths) for faster startup in future invocations.

Note that due to the number of experiments being run by the above command, we recommend execution on a compute cluster.
The GeneDisco codebase also supports execution on slurm compute clusters (the slurm command must be available on the executing node) using the following command and using dependencies in a Python virtualenv available at /path/to/your/virtualenv (please replace with your own virtualenv path):

run_experiments \
  --cache_directory=/path/to/genedisco_cache  \
  --output_directory=/path/to/genedisco_output  \
  --acquisition_batch_size=64  \
  --num_active_learning_cycles=8  \
  --schedule_on_slurm \
  --schedule_children_on_slurm \
  --remote_execution_virtualenv_path=/path/to/your/virtualenv

Other scheduling systems are currently not supported by default.

How to Run A Single Isolated Experiment (One Learning Cycle)?

To run one active learning loop cycle, for example, with the "topuncertain" acquisition function, the "achilles" feature set and the "schmidt_2021_ifng" task, execute the following command:

active_learning_loop  \
    --cache_directory=/path/to/genedisco/genedisco_cache \
    --output_directory=/path/to/genedisco/genedisco_output \
    --model_name="bayesian_mlp" \
    --acquisition_function_name="topuncertain" \
    --acquisition_batch_size=64 \
    --num_active_learning_cycles=8 \
    --feature_set_name="achilles" \
    --dataset_name="schmidt_2021_ifng" 

How to Evaluate a Custom Acquisition Function?

To run a custom acquisition function, set --acquisition_function_name="custom" and --acquisition_function_path to the file path that contains your custom acquisition function.

active_learning_loop  \
    --cache_directory=/path/to/genedisco/genedisco_cache \
    --output_directory=/path/to/genedisco/genedisco_output \
    --model_name="bayesian_mlp" \
    --acquisition_function_name="custom" \
    --acquisition_function_path=/path/to/custom_acquisition_function.py \
    --acquisition_batch_size=64 \
    --num_active_learning_cycles=8 \
    --feature_set_name="achilles" \
    --dataset_name="schmidt_2021_ifng" 

...where "/path/to/custom_acquisition_function.py" contains code for your custom acquisition function corresponding to the BaseBatchAcquisitionFunction interface, e.g.:

import numpy as np
from typing import AnyStr, List
from slingpy import AbstractDataSource
from slingpy.models.abstract_base_model import AbstractBaseModel
from genedisco.active_learning_methods.acquisition_functions.base_acquisition_function import \
    BaseBatchAcquisitionFunction

class RandomBatchAcquisitionFunction(BaseBatchAcquisitionFunction):
    def __call__(self,
                 dataset_x: AbstractDataSource,
                 batch_size: int,
                 available_indices: List[AnyStr], 
                 last_selected_indices: List[AnyStr] = None, 
                 model: AbstractBaseModel = None,
                 temperature: float = 0.9,
                 ) -> List:
        selected = np.random.choice(available_indices, size=batch_size, replace=False)
        return selected

Note that the last class implementing BaseBatchAcquisitionFunction is loaded by GeneDisco if there are multiple valid acquisition functions present in the loaded file.

Citation

Please consider citing, if you reference or use our methodology, code or results in your work:

@inproceedings{mehrjou2022genedisco,
    title={{GeneDisco: A Benchmark for Experimental Design in Drug Discovery}},
    author={Mehrjou, Arash and Soleymani, Ashkan and Jesson, Andrew and Notin, Pascal and Gal, Yarin and Bauer, Stefan and Schwab, Patrick},
    booktitle={{International Conference on Learning Representations (ICLR)}},
    year={2022}
}

License

License

Authors

Patrick Schwab, GlaxoSmithKline plc
Arash Mehrjou, GlaxoSmithKline plc
Andrew Jesson, University of Oxford
Ashkan Soleymani, MIT

Acknowledgements

PS and AM are employees and shareholders of GlaxoSmithKline plc.