Abstract
Most approaches for predicting drug-drug interactions (DDIs) have focused on text. We present the first work that uses multiple drug structure data - images, string representations and relationship representations. We exploit the recent advances in deep networks to integrate these varied sources of inputs in predicting DDIs. Our empirical evaluations clearly demonstrate the efficacy of combining heterogeneous data in predicting DDIs.
D.S. Dhami and S. Yan—Equal contribution.
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Acknowledgements
We gratefully acknowledge DARPA Minerva award FA9550-19-1-0391. Any opinions, findings, and conclusion or recommendations expressed are those of the authors and do not necessarily reflect the view of the AFOSR, DARPA or the US government.
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Dhami, D.S., Yan, S., Kunapuli, G., Page, D., Natarajan, S. (2021). Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Riaño, D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science(), vol 12721. Springer, Cham. https://doi.org/10.1007/978-3-030-77211-6_28
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DOI: https://doi.org/10.1007/978-3-030-77211-6_28
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