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Uno
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Unified Drug Response Predictor

Model Architecture
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UNO consists of two fully connected neural network branches for separately embedding the drug and cell line features, then a third fully connected neural network to regress on the combined embedded features. UNO was one of the benchmark models in the CANDLE project for cancer drug response.

Feature Representation
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   * Cancer features: 

      * Gene Expression: as floats

   * Drug features: 

      * Drug Molecular Descriptors: mordred values as floats


URLs
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- `Original GitHub <https://github.com/JDACS4C-IMPROVE/Benchmarks/tree/preprocess_improve/Pilot1/Uno_IMPROVE>`__

References
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`1. <https://academic.oup.com/bib/article/23/1/bbab356/6370300>`_ F. Xia, et. al. "A cross-study analysis of drug response prediction in cancer cell lines", Briefings in Bioinformatics, 2022