DeepSide: A Deep Learning Framework for Drug Side Effect Prediction

Uner, Onur Can; Cinbiş, Ramazan Gökberk; Taştan, Öznur; Çiçek, A. Ercüment

doi:10.1101/843029

Cited by 23 publications

(22 citation statements)

References 35 publications

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“…CNNs have also proven useful for processing 1D data. Some examples are drug chemical structure representation (e.g., SMILES [ 35 , 36 ]), natural language (i.e., sentences [ 37 ]) and EEG signals [ 38 ]. Thus, we conjectured that a CNN is a good candidate for the classification tasks mentioned above.…”

Section: Methodsmentioning

confidence: 99%

Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy

et al. 2020

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Complete resection of the tumor is important for survival in glioma patients. Even if the gross total resection was achieved, left-over micro-scale tissue in the excision cavity risks recurrence. High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) technique can distinguish healthy and malign tissue efficiently using peak intensities of biomarker metabolites. The method is fast, sensitive and can work with small and unprocessed samples, which makes it a good fit for real-time analysis during surgery. However, only a targeted analysis for the existence of known tumor biomarkers can be made and this requires a technician with chemistry background, and a pathologist with knowledge on tumor metabolism to be present during surgery. Here, we show that we can accurately perform this analysis in real-time and can analyze the full spectrum in an untargeted fashion using machine learning. We work on a new and large HRMAS NMR dataset of glioma and control samples (n = 565), which are also labeled with a quantitative pathology analysis. Our results show that a random forest based approach can distinguish samples with tumor cells and controls accurately and effectively with a median AUC of 85.6% and AUPR of 93.4%. We also show that we can further distinguish benign and malignant samples with a median AUC of 87.1% and AUPR of 96.1%. We analyze the feature (peak) importance for classification to interpret the results of the classifier. We validate that known malignancy biomarkers such as creatine and 2-hydroxyglutarate play an important role in distinguishing tumor and normal cells and suggest new biomarker regions. The code is released at http://github.com/ciceklab/HRMAS_NC.

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Section: Methodsmentioning

confidence: 99%

Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy

et al. 2020

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“…CNNs have also proven useful for processing 1D data. Some examples are drug chemical structure representation (e.g., SMILES [10, 37]), natural language (i.e., sentences [38]) and EEG signals [35]. Thus, we conjectured that a CNN is a good candidate for the classification tasks mentioned above.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Assisted Intraoperative Assessment of Brain Tumor Margins Using HRMAS NMR Spectroscopy

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et al. 2020

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Complete resection of the tumor is important for survival in glioma patients. Even if the gross total resection was achieved, left-over micro-scale tissue in the excision cavity risks recurrence. High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) technique can distinguish healthy and malign tissue efficiently using peak intensities of biomarker metabolites. The method is fast, sensitive and can work with small and unprocessed samples, which makes it a good fit for real-time analysis during surgery. However, only a targeted analysis for the existence of known tumor biomarkers can be made and this requires a technician with chemistry background, and a pathologist with knowledge on tumor metabolism to be present during surgery. Here, we show that we can accurately perform this analysis in real-time and can analyze the full spectrum in an untargeted fashion using machine learning. We work on a new and large HRMAS NMR dataset of glioma and control samples (n = 568), which are also labeled with a quantitative pathology analysis. Our results show that a random forest based approach can distinguish samples with tumor cells and controls accurately and effectively with a mean AUC of 85.6% and AUPR of 93.4%. We also show that we can further distinguish benign and malignant samples with a mean AUC of 87.1% and AUPR of 96.1%. We analyze the feature (peak) importance for classification to interpret the results of the classifier. We validate that known malignancy biomarkers such as creatine and 2-hydroxyglutarate play an important role in distinguish tumor and normal cells and suggest new biomarker regions. The code is released at http://github.com/ciceklab/HRMAS_NC.

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“…This means accurate and efficient computational prioritization of novel synergistic drug pair candidates will keep being an important research area. In MatchMaker, we use only the drug chemical structure as the primary feature, which has been used in parallel tasks such as drug target identification [50] or drug side effect prediction [51,52]. We also use the cell line specific gene expression profile to capture the context of the experiment.…”

Section: Discussionmentioning

confidence: 99%

MatchMaker: A Deep Learning Framework for Drug Synergy Prediction

Kuru

Taştan

Çiçek

2020

Preprint

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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 highthroughout 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 ∼ 20% correlation and ∼ 40% 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

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DeepSide: A Deep Learning Framework for Drug Side Effect Prediction

Cited by 23 publications

References 35 publications

Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy

Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy

Machine Learning Assisted Intraoperative Assessment of Brain Tumor Margins Using HRMAS NMR Spectroscopy

MatchMaker: A Deep Learning Framework for Drug Synergy Prediction

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