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@@ -18,7 +18,7 @@ configs:
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  path: data/train-*
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  ---
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- To accompany OpenPhenom, Recursion is releasing the **RxRx3-core** dataset, a challenge dataset in phenomics optimized for the research community.
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  RxRx3-core includes labeled images of 735 genetic knockouts and 1,674 small-molecule perturbations drawn from the [RxRx3 dataset](https://www.rxrx.ai/rxrx3),
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  image embeddings computed with [OpenPhenom](https://huggingface.co/recursionpharma/OpenPhenom), and associations between the included small molecules and genes.
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  The dataset contains 6-channel Cell Painting images and associated embeddings from 222,601 wells but is less than 18Gb, making it incredibly accessible to the research community.
@@ -27,6 +27,10 @@ Mapping the mechanisms by which drugs exert their actions is an important challe
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  We are excited to release the first dataset of this scale probing concentration-response along with a benchmark and model to enable the research community to
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  rapidly advance this space.
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  Loading the RxRx3-core image dataset
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  ```
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  from datasets import load_dataset
@@ -43,4 +47,3 @@ file_path_embs = hf_hub_download("recursionpharma/rxrx3-core", filename="OpenPhe
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  open_phenom_embeddings = pd.read_parquet(file_path_embs)
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  rxrx3_core_metadata = pd.read_csv(file_path_metadata)
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  ```
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- Benchmarking code for this dataset is provided in the [EFAAR benchmarking repo](https://github.com/recursionpharma/EFAAR_benchmarking/tree/trunk/RxRx3-core_benchmarks).
 
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  path: data/train-*
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  ---
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+ To accompany OpenPhenom, Recursion is releasing the [**RxRx3-core**](https://arxiv.org/abs/2503.20158) dataset, a challenge dataset in phenomics optimized for the research community.
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  RxRx3-core includes labeled images of 735 genetic knockouts and 1,674 small-molecule perturbations drawn from the [RxRx3 dataset](https://www.rxrx.ai/rxrx3),
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  image embeddings computed with [OpenPhenom](https://huggingface.co/recursionpharma/OpenPhenom), and associations between the included small molecules and genes.
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  The dataset contains 6-channel Cell Painting images and associated embeddings from 222,601 wells but is less than 18Gb, making it incredibly accessible to the research community.
 
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  We are excited to release the first dataset of this scale probing concentration-response along with a benchmark and model to enable the research community to
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  rapidly advance this space.
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+ Paper published at LMRL Workshop at ICLR 2025 [RxRx3-core: Benchmarking drug-target interactions in High-Content Microscopy](https://arxiv.org/abs/2503.20158).
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+ Benchmarking code for this dataset is provided in the [EFAAR benchmarking repo](https://github.com/recursionpharma/EFAAR_benchmarking/tree/trunk/RxRx3-core_benchmarks) and [Polaris](https://polarishub.io/benchmarks/recursion/rxrx-compound-gene-activity-benchmark).
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+
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+ ---
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  Loading the RxRx3-core image dataset
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  ```
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  from datasets import load_dataset
 
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  open_phenom_embeddings = pd.read_parquet(file_path_embs)
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  rxrx3_core_metadata = pd.read_csv(file_path_metadata)
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  ```