We lifted 9 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| uhlerlab/infocore | canonical | 3 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| new_drug_sampler | Ran | uhlerlab/infocore/GE/dataloader.py pointer only (licence: NONE) · get_code("7f20b62725f929e6") |
| str2bool | Ran | uhlerlab/infocore/CP/utils.py pointer only (licence: NONE) · get_code("7c508037b40522af") |
| uniformity_loss | Ran | uhlerlab/infocore/CP/metrics.py pointer only (licence: NONE) · get_code("0a8d860dc9db241a") |
| cov_loss | Not yet run | uhlerlab/infocore/CP/metrics.py pointer only (licence: NONE) · get_code("65944d9f277175bc") |
| dataset2batchsubset | Not yet run | uhlerlab/infocore/CP/dataloader.py pointer only (licence: NONE) · get_code("2b516aedcfb605a6") |
| dataset2batchsubset | Not yet run | uhlerlab/infocore/GE/dataloader.py pointer only (licence: NONE) · get_code("1b53d4547f5385d4") |
| dataset2cellsubset | Not yet run | uhlerlab/infocore/GE/dataloader.py pointer only (licence: NONE) · get_code("54e8481670fe2a27") |
| dict2torch | Not yet run | uhlerlab/infocore/CP/utils.py pointer only (licence: NONE) · get_code("ab613ef60aebbb62") |
| select_inst | Not yet run | uhlerlab/infocore/CP/utils.py pointer only (licence: NONE) · get_code("7d6c7db42a39ac20") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
High-throughput drug screening -- using cell imaging or gene expression measurements as readouts of drug effect -- is a critical tool in biotechnology to assess and understand the relationship between the chemical structure and biological activity of a drug. Since large-scale screens have to be divided into multiple experiments, a key difficulty is dealing with batch effects, which can introduce systematic errors and non-biological associations in the data. We propose InfoCORE, an Information maximization approach for COnfounder REmoval, to effectively deal with batch effects and obtain refined molecular representations. InfoCORE establishes a variational lower bound on the conditional mutual information of the latent representations given a batch identifier. It adaptively reweighs samples to equalize their implied batch distribution. Extensive experiments on drug screening data reveal InfoCORE's superior performance in a multitude of tasks including molecular property prediction and molecule-phenotype retrieval. Additionally, we show results for how InfoCORE offers a versatile framework and resolves general distribution shifts and issues of data fairness by minimizing correlation with spurious features or removing sensitive attributes. The code is available at https://github.com/uhlerlab/InfoCORE.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2312.00718")
get_code_for_paper("2312.00718")
have("2312.00718")
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