Dataset Information

Single-Cell Techniques and Deep Learning in Predicting Drug Response.

ABSTRACT: Rapidly developing single-cell sequencing analyses produce more comprehensive profiles of the genomic, transcriptomic, and epigenomic heterogeneity of tumor subpopulations than do traditional bulk sequencing analyses. Moreover, single-cell techniques allow the response of a tumor to drug exposure to be more thoroughlyinvestigated. Deep learning (DL) models have successfully extracted features from complex bulk sequence data to predict drug responses. We review recent innovations in single-cell technologies and DL-based approaches related to drug sensitivity predictions. We believe that, by using insights from bulk sequencedata, deep transfer learning (DTL) can facilitate the use of single-cell data for training superior DL-based drug prediction models.

SUBMITTER: Wu Z

PROVIDER: S-EPMC7669610 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Single-Cell Techniques and Deep Learning in Predicting Drug Response.

Wu Zhenyu Z Lawrence Patrick J PJ Ma Anjun A Zhu Jian J Xu Dong D Ma Qin Q

Trends in pharmacological sciences 20201102 12

Rapidly developing single-cell sequencing analyses produce more comprehensive profiles of the genomic, transcriptomic, and epigenomic heterogeneity of tumor subpopulations than do traditional bulk sequencing analyses. Moreover, single-cell techniques allow the response of a tumor to drug exposure to be more thoroughlyinvestigated. Deep learning (DL) models have successfully extracted features from complex bulk sequence data to predict drug responses. We review recent innovations in single-cell t ...[more]

PMID: 33153777

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Project description:Motivation:While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a time- and cost-efficient way to prioritize combinations to test, based on recently available large-scale combination screening data. Recently, Deep Learning has had an impact in many research areas by achieving new state-of-the-art model performance. However, Deep Learning has not yet been applied to drug synergy prediction, which is the approach we present here, termed DeepSynergy. DeepSynergy uses chemical and genomic information as input information, a normalization strategy to account for input data heterogeneity, and conical layers to model drug synergies. Results:DeepSynergy was compared to other machine learning methods such as Gradient Boosting Machines, Random Forests, Support Vector Machines and Elastic Nets on the largest publicly available synergy dataset with respect to mean squared error. DeepSynergy significantly outperformed the other methods with an improvement of 7.2% over the second best method at the prediction of novel drug combinations within the space of explored drugs and cell lines. At this task, the mean Pearson correlation coefficient between the measured and the predicted values of DeepSynergy was 0.73. Applying DeepSynergy for classification of these novel drug combinations resulted in a high predictive performance of an AUC of 0.90. Furthermore, we found that all compared methods exhibit low predictive performance when extrapolating to unexplored drugs or cell lines, which we suggest is due to limitations in the size and diversity of the dataset. We envision that DeepSynergy could be a valuable tool for selecting novel synergistic drug combinations. Availability and implementation:DeepSynergy is available via www.bioinf.jku.at/software/DeepSynergy. Contact:klambauer@bioinf.jku.at. Supplementary information:Supplementary data are available at Bioinformatics online.

Dataset Information

Single-Cell Techniques and Deep Learning in Predicting Drug Response.

Publications

Single-Cell Techniques and Deep Learning in Predicting Drug Response.

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