Opportunities and obstacles for deep learning in biology and medicine

@article{Ching2017OpportunitiesAO,
  title={Opportunities and obstacles for deep learning in biology and medicine},
  author={Travers Ching and Daniel S. Himmelstein and Brett K. Beaulieu-Jones and Alexandr A Kalinin and Brian T Do and Gregory P. Way and Enrico Ferrero and Paul-Michael Agapow and Michael Zietz and Michael M. Hoffman and Wei Xie and Gail L. Rosen and Benjamin J. Lengerich and Johnny Israeli and Jack Lanchantin and Stephen Woloszynek and Anne E Carpenter and Avanti Shrikumar and Jinbo Xu and Evan M. Cofer and Christopher A. Lavender and Srinivas C. Turaga and Amr M. Alexandari and Zhiyong Lu and David J Harris and David DeCaprio and Yanjun Qi and Anshul Kundaje and Yifan Peng and Laura K. Wiley and Marwin H. S. Segler and Simina Maria Boca and S. Joshua Joshua Swamidass and Austin Huang and Anthony Gitter and Casey S. Greene},
  journal={Journal of the Royal Society Interface},
  year={2017},
  volume={15}
}
Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. [] Key Result Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress…
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References

SHOWING 1-10 OF 806 REFERENCES
Deep learning for healthcare: review, opportunities and challenges
TLDR
It is suggested that deep learning approaches could be the vehicle for translating big biomedical data into improved human health and develop holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.
Applications of Deep Learning in Biomedicine.
TLDR
Key features of deep learning that may give this approach an edge over other machine learning methods are discussed and a number of applications ofdeep learning in biomedical studies demonstrating proof of concept and practical utility are reviewed.
Deep learning in bioinformatics
TLDR
This review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies and suggest future research directions.
Low Data Drug Discovery with One-Shot Learning
TLDR
This work demonstrates how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications and introduces a new architecture, the iterative refinement long short-term memory, that significantly improves learning of meaningful distance metrics over small-molecules.
Deep learning for computational biology
TLDR
This review discusses applications of this new breed of analysis approaches in regulatory genomics and cellular imaging, and provides background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights.
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain
TLDR
A novel knowledge-distillation approach called Interpretable Mimic Learning is introduced, to learn interpretable phenotype features for making robust prediction while mimicking the performance of deep learning models.
Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data
TLDR
From episodic, longitudinal sequences of serum uric acid measurements in 4368 individuals, this work produced continuous phenotypic features that suggest multiple population subtypes, and that accurately distinguished the uric-acid signatures of gout vs. acute leukemia despite not being optimized for the task.
Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules
TLDR
A brief overview of deep learning methods is presented and in particular how recursive neural network approaches can be applied to the problem of predicting molecular properties, by considering an ensemble of recursive neural networks associated with all possible vertex-centered acyclic orientations of the molecular graph.
Diet Networks: Thin Parameters for Fat Genomics
TLDR
A novel neural network parametrization is proposed which considerably reduces the number of parameters and the error rate of the classifier on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms, yielding millions of ternary inputs.
Multi-Layer and Recursive Neural Networks for Metagenomic Classification
TLDR
Traditional neural networks can be quite powerful classifiers on metagenomic data compared to baseline methods, such as random forests, and deep learning approaches did not result in improvements to the classification accuracy, but they do provide the ability to learn hierarchical representations of a data set that standard classification methods do not allow.
...
...