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Deep learning in systems medicine.

Haiying Wang1, Estelle Pujos-Guillot2, Blandine Comte3

  • 1computer science at Ulster University.

Briefings in Bioinformatics
|November 16, 2020
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Summary
This summary is machine-generated.

Systems medicine (SM) leverages deep learning (DL) for complex disease research. This review explores DL applications in SM for predictive, preventive, and precision medicine, highlighting challenges and opportunities.

Keywords:
biomarker discoverydata integrationdeep learning (DL)disease classificationsystems medicine (SM)

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Area of Science:

  • Systems Medicine
  • Computational Biology
  • Bioinformatics

Background:

  • Systems medicine (SM) offers a holistic approach to understanding complex diseases.
  • High-dimensional, heterogeneous data presents challenges in disease research.
  • Deep learning (DL) shows promise in extracting meaningful features from complex biological data.

Purpose of the Study:

  • To review key developments in deep learning (DL) algorithms relevant to systems medicine (SM).
  • To explore the application of DL in predictive, preventive, and precision medicine within the SM framework.
  • To identify challenges and opportunities for DL adoption in SM research.

Main Methods:

  • Literature review of deep learning algorithms and their applications in systems medicine.
  • Analysis of DL's role in predictive, preventive, and precision medicine.
  • Case study examples, including a personalized Parkinson's disease model.

Main Results:

  • DL algorithms are crucial for analyzing complex, high-dimensional data in SM.
  • DL facilitates advancements in predictive, preventive, and personalized medicine.
  • Key challenges include achieving clinical impact and enhancing model interpretability.

Conclusions:

  • Deep learning holds significant potential to advance systems medicine research and clinical applications.
  • Further research is needed to address interpretability and clinical translation challenges.
  • DL integration in SM can lead to improved understanding and treatment of complex diseases.