Deepside: predicting drug side effects with deep learning

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buir.advisorÇicek, A. Ercüment
dc.contributor.authorÜner, Onur Can
dc.date.accessioned2019-10-21T13:27:33Z
dc.date.available2019-10-21T13:27:33Z
dc.date.copyright2019-09
dc.date.issued2019-09
dc.date.submitted2019-10-18
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2019.en_US
dc.descriptionIncludes bibliographical references (leaves 41-46).en_US
dc.description.abstractDrug failures due to unforeseen adverse effects at clinical trials pose health risks for the participants and cause substantial financial losses. Side effect prediction algorithms, on the other hand, have the potential to guide the drug design process. LINCS L1000 dataset provides a vast resource of gene expression profiles across different cell lines that are induced with different dosages taken at different time points. The state-of-the-art approach in the literature relies on high-quality experiments in LINCS L1000 and discard a large portion of the recorded experiments. In this study, we investigate whether more information can be extracted from this remaining set of experiments with a deep learning-based approach. We experiment with 6 different deep learning architectures that use (i) gene expression data from the LINCS L1000 project, (ii) chemical structure fingerprints of drugs, (iii) SMILES string representation of drug structure, and (iv) the atomic structure of the drug molecules. The multilayer perceptron (MLP) based model which uses chemical structures and gene expression features achieve 88% micro- AUC and 79% macro-AUC, thus offering better performance in comparison to the state-of-the-art studies on side effect prediction. We observe that the chemical structure is more predictive than the gene expression profiles despite the fact that the features are extracted with different deep learning models. Finally, the convolutional neural network-based model that uses only SMILES strings of the drugs provides 82% macro-AUC, and 88%micro-AUC improvements, better performing than the models that use gene expression and chemical structure features simultaneously.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityby Onur Can Üneren_US
dc.format.extentxi, 46 leaves : charts (some color) ; 30 cm.en_US
dc.identifier.itemidB160170
dc.identifier.urihttp://hdl.handle.net/11693/52688
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMachine learningen_US
dc.subjectDeep learningen_US
dc.subjectMolecular biologyen_US
dc.subjectADRen_US
dc.subjectSide effectsen_US
dc.subjectDrugsen_US
dc.subjectCNNen_US
dc.subjectMLPen_US
dc.titleDeepside: predicting drug side effects with deep learningen_US
dc.title.alternativeDeepside: ilaçların ters ve yan etkilerini tahmin etmek için derin öğrenmeen_US
dc.typeThesisen_US

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