Skip to main content

Next-Generation Machine Learning

New Content ItemEdited by Jason Moore and Marylyn Ritchie

The Editors of BioData Mining seek manuscripts for our new collection on the topic of machine learning. 
We are interested in both original research and review papers, especially those that address new and novel machine learning methods and their application to biological and biomedical big data.
Specific topics of interest include, but are not limited to:

  • Automated machine learning
  • Better benchmarks
  • Bioinformatics applications
  • Clinical informatics applications
  • Deep learning
  • Expert knowledge
  • Feature engineering
  • Feature selection
  • Knowledge engineering
  • Model interpretation
  • Transferability

Manuscripts should be formatted according to our submission guidelines and submitted via the online submission system. In the submission system please make sure the correct collection title is chosen in the “Questionnaire” section. Please also indicate clearly in the covering letter that the manuscript is to be considered for the "Next-generation machine learning" series.

This collection of articles has not been sponsored and articles have undergone the journal’s standard peer-review process. Non-commissioned submissions will be considered.

You can submit to this series, here.

  1. Accurate identification of acute ischemic stroke (AIS) patient cohorts is essential for a wide range of clinical investigations. Automated phenotyping methods that leverage electronic health records (EHRs) rep...

    Authors: Phyllis M. Thangaraj, Benjamin R. Kummer, Tal Lorberbaum, Mitchell S. V. Elkind and Nicholas P. Tatonetti

    Citation: BioData Mining 2020 13:21

    Content type: Research

    Published on: