Deep Learning for Biomedical and Health Informatics (Closed)

Deep Learning for Biomedical and Health Informatics

Download the Call-for-Papers (PDF) for this special issue.

Important Dates:

  • Extended deadline for Submission: 11 July 2016
  • Submission Opens: 20 June 2016
  • First Reviews Due: 8 August 2016
  • Revised Manuscript Due: 19 September 2016
  • Final Decision: 10 October 2016


Deep learning is a rapidly advancing field in recent years, in terms of both methodological development and practical applications. It allows computational models of multiple processing layers to learn and represent data with multiple levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and ideally suited to some of the hardware architectures that are currently available.

Whilst some of the technical challenges are still being addressed, including generative modelling, large-scale parameter optimisation, and handling heterogeneous multi-modal data with varying temporal dependencies and missing samples, its use for biomedical and health informatics has reached marked success. Examples include the use of deep learning for imaging informatics, with which deep convolutional networks have dramatically improved the analysis performance compared to that of existing techniques. Other applications include drug discovery, protein docking and structure prediction, genomics, and annotating the pathogenicity of genetic variants.

The purpose of this special issue is to report the latest advances in the field of deep learning for biomedical and health informatics, addressing both original algorithmic development and new applications of deep learning. Topics for this special issue include, but are not limited to:

  • Deep learning for sensor informatics and behavioural/activity profiling
  • Deep learning for imaging informatics and large-scale mining/classification
  • Deep learning for translational bioinformatics and drug discovery
  • Deep learning for medical informatics and public health

We particularly encourage large cohort studies with clearly demonstrated clinical translational values supplemented by online data sets or algorithms that can be shared by the research community.

For more information, please refer to the Call-for-Papers (PDF) for this special issue.



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