DeepSide: a deep learning approach for drug side effect prediction

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buir.contributor.authorÜner, Onur Can
buir.contributor.authorKuru, Halil İbrahim
buir.contributor.authorÇiçek, A. Ercüment
buir.contributor.orcidCicek, A. Ercument|0000-0001-8613-6619
dc.citation.epage339en_US
dc.citation.issueNumber1en_US
dc.citation.spage330en_US
dc.citation.volumeNumber20en_US
dc.contributor.authorÜner, Onur Can
dc.contributor.authorKuru, Halil İbrahim
dc.contributor.authorCinbiş, R. Gökberk
dc.contributor.authorTaştan, Öznur
dc.contributor.authorÇiçek, A. Erüment
dc.date.accessioned2023-02-15T11:54:51Z
dc.date.available2023-02-15T11:54:51Z
dc.date.issued2022-01-07
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractDrug 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 .en_US
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2023-02-15T11:54:51Z No. of bitstreams: 1 DeepSide_A_Deep_Learning_Approach_for_Drug_Side_Effect_Prediction.pdf: 930660 bytes, checksum: 737e596f91df743d10dcab8fc05914b7 (MD5)en
dc.description.provenanceMade available in DSpace on 2023-02-15T11:54:51Z (GMT). No. of bitstreams: 1 DeepSide_A_Deep_Learning_Approach_for_Drug_Side_Effect_Prediction.pdf: 930660 bytes, checksum: 737e596f91df743d10dcab8fc05914b7 (MD5) Previous issue date: 2022-01-07en
dc.identifier.doi10.1109/TCBB.2022.3141103en_US
dc.identifier.eissn1557-9964
dc.identifier.issn1545-5963
dc.identifier.urihttp://hdl.handle.net/11693/111348
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttps://www.doi.org/10.1109/TCBB.2022.3141103en_US
dc.source.titleIEEE Transactions on Communicationsen_US
dc.subjectDrug side effect predictionen_US
dc.subjectDeep learningen_US
dc.subjectLINCSen_US
dc.titleDeepSide: a deep learning approach for drug side effect predictionen_US
dc.typeArticleen_US

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