MatchMaker: A deep learning framework for drug synergy prediction
buir.contributor.author | Kuru, Halil İbrahim | |
buir.contributor.author | Çiçek, Ercüment | |
buir.contributor.orcid | Kuru, Halil İbrahim|0000-0003-4356-8846 | |
buir.contributor.orcid | Çiçek, Ercüment|0000-0001-8613-6619 | |
dc.contributor.author | Kuru, Halil İbrahim | |
dc.contributor.author | Taştan, Ö. | |
dc.contributor.author | Çiçek, Ercüment | |
dc.date.accessioned | 2022-01-28T06:28:48Z | |
dc.date.available | 2022-01-28T06:28:48Z | |
dc.date.issued | 2021-06-04 | |
dc.department | Department of Computer Engineering | en_US |
dc.description | ( Early Access ) | en_US |
dc.description.abstract | Drug combination therapies have been a viable strategy for the treatment of complex diseases such as cancer due to increased efficacy and reduced side effects. However, experimentally validating all possible combinations for synergistic interaction even with high-throughout screens is intractable due to vast combinatorial search space. Computational techniques can reduce the number of combinations to be evaluated experimentally by prioritizing promising candidates. We present MatchMaker that predicts drug synergy scores using drug chemical structure information and gene expression profiles of cell lines in a deep learning framework. For the first time, our model utilizes the largest known drug combination dataset to date, DrugComb. We compare the performance of MatchMaker with the state-of-the-art models and observe up to 15% correlation and 33% mean squared error (MSE) improvements over the next best method. We investigate the cell types and drug pairs that are relatively harder to predict and present novel candidate pairs. MatchMaker is built and available at https://github.com/tastanlab/matchmaker | en_US |
dc.description.provenance | Submitted by Evrim Ergin (eergin@bilkent.edu.tr) on 2022-01-28T06:28:48Z No. of bitstreams: 1 MatchMaker_A_deep_learning_framework_for_drug_synergy_prediction.pdf: 2047324 bytes, checksum: a00da33b4c35ac65cf5ff2c26bcb3656 (MD5) | en |
dc.description.provenance | Made available in DSpace on 2022-01-28T06:28:48Z (GMT). No. of bitstreams: 1 MatchMaker_A_deep_learning_framework_for_drug_synergy_prediction.pdf: 2047324 bytes, checksum: a00da33b4c35ac65cf5ff2c26bcb3656 (MD5) Previous issue date: 2021-06-04 | en |
dc.identifier.doi | 10.1109/TCBB.2021.3086702 | en_US |
dc.identifier.eissn | 1557-9964 | en_US |
dc.identifier.issn | 1545-5963 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/76852 | en_US |
dc.language.iso | English | en_US |
dc.publisher | IEEE | en_US |
dc.relation.isversionof | https://doi.org/10.1109/TCBB.2021.3086702 | en_US |
dc.source.title | IEEE/ACM Transactions on Computational Biology and Bioinformatics | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Drug synergy | en_US |
dc.subject | Chemical features | en_US |
dc.subject | Cancer cell lines | en_US |
dc.title | MatchMaker: A deep learning framework for drug synergy prediction | en_US |
dc.type | Article | en_US |
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