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      DeepSide: a deep learning approach for drug side effect prediction

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      Author(s)
      Üner, Onur Can
      Kuru, Halil İbrahim
      Cinbiş, R. Gökberk
      Taştan, Öznur
      Çiçek, A. Erüment
      Date
      2022-01-07
      Source Title
      IEEE Transactions on Communications
      Print ISSN
      1545-5963
      Electronic ISSN
      1557-9964
      Publisher
      IEEE
      Volume
      20
      Issue
      1
      Pages
      330 - 339
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      Drug failures due to unforeseen adverse effects at clinical trials pose health risks for the participants and lead to substantial financial losses. Side effect prediction algorithms have the potential to guide the drug design process. LINCS L1000 dataset provides a vast resource of cell line gene expression data perturbed by different drugs and creates a knowledge base for context specific features. The state-of-the-art approach that aims at using context specific information relies on only the high-quality experiments in LINCS L1000 and discards a large portion of the experiments. In this study, our goal is to boost the prediction performance by utilizing this data to its full extent. We experiment with 5 deep learning architectures. We find that a multi-modal architecture produces the best predictive performance among multi-layer perceptron-based architectures when drug chemical structure (CS), and the full set of drug perturbed gene expression profiles (GEX) are used as modalities. Overall, we observe that the CS is more informative than the GEX. A convolutional neural network-based model that uses only SMILES string representation of the drugs achieves the best results and provides 13.0% macro-AUC and 3.1% micro-AUC improvements over the state-of-the-art. We also show that the model is able to predict side effect-drug pairs that are reported in the literature but was missing in the ground truth side effect dataset. DeepSide is available at http://github.com/OnurUner/DeepSide .
      Keywords
      Drug side effect prediction
      Deep learning
      LINCS
      Permalink
      http://hdl.handle.net/11693/111348
      Published Version (Please cite this version)
      https://www.doi.org/10.1109/TCBB.2022.3141103
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