Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification

@article{Kalinin2018DeepLI,
  title={Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification},
  author={Alexandr A Kalinin and Gerald A. Higgins and Narathip Reamaroon and S. Mohamad R. Soroushmehr and Ari Allyn-Feuer and Ivo D. Dinov and Kayvan Najarian and Brian D. Athey},
  journal={Pharmacogenomics},
  year={2018},
  volume={19 7},
  pages={
          629-650
        }
}
This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulatory variants located in noncoding domains of the genome and their function as applied to pharmacoepigenomics; patient stratification from medical records; and the mechanistic prediction of drug response, targets and their interactions. Deep learning encapsulates a family of machine learning algorithms that has transformed many important subfields… 

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References

SHOWING 1-10 OF 134 REFERENCES

DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing

TLDR
DeepMetabolism, a biology-guided deep learning system to predict cell phenotypes from transcriptomics data, is developed to bridge the gap between genotype and phenotype and to serve as a springboard for applications in synthetic biology and precision medicine.

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data.

TLDR
This work demonstrates a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions and proposes using deep neural net confusion matrices for drug repositioning.

Cancer pharmacogenomics, challenges in implementation, and patient-focused perspectives

  • J. Patel
  • Biology, Medicine
    Pharmacogenomics and personalized medicine
  • 2016
TLDR
This review provides an update on cancer pharmacogenomics and genomics-based medicine, challenges in applying Pharmacogenomics to the clinical setting, and patient perspectives on the use of pharmacogenetics to personalize cancer therapy.

Pharmacogenomics in clinical practice and drug development

TLDR
Adoption of GWAS, exome or whole genome sequencing by drug development and treatment programs is the most striking near-term opportunity for improving the drug candidate pipeline and boosting the efficacy of medications already in use.

Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets

TLDR
An introduction to machine learning tasks that address important problems in genomic medicine by focusing on how machine learning can help to model the relationship between DNA and the quantities of key molecules in the cell, with the premise that these quantities may be associated with disease risks.

Deep Learning and Association Rule Mining for Predicting Drug Response in Cancer. A Personalised Medicine Approach

TLDR
It is demonstrated that DLNNs trained on a large pharmacogenomics data-set can effectively predict the therapeutic response of specific drugs in different cancer types, a milestone in precision medicine.

DL-ADR: a novel deep learning model for classifying genomic variants into adverse drug reactions

TLDR
A novel deep learning model based on generative stochastic networks and hidden Markov chain to classify the observed samples with SNPs on five loci of two genes respectively to the vulnerable population of 14 types of adverse reactions is presented.

Genomics and transcriptomics in drug discovery.

Pharmacogenomics in cardiology--genetics and drug response: 10 years of progress.

TLDR
The Translational Pharmacogenetics Project was formed as a network-wide PGRN effort to translate actionable pharmacogenetic discoveries into clinical practice, and the NIH-funded Implementing Genomics in Practice Network was formed to further enhance and accelerate the incorporation of genomic information into clinical care.
...