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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. Deep learning models can infer cancer patient prognosis from molecular and anatomic pathology information. Recent studies that leveraged information from complementary multimodal data improved prognostication,...

    Authors: Zarif L. Azher, Anish Suvarna, Ji-Qing Chen, Ze Zhang, Brock C. Christensen, Lucas A. Salas, Louis J. Vaickus and Joshua J. Levy
    Citation: BioData Mining 2023 16:23
  2. Quantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort ...

    Authors: Philip J. Freda, Attri Ghosh, Elizabeth Zhang, Tianhao Luo, Apurva S. Chitre, Oksana Polesskaya, Celine L. St. Pierre, Jianjun Gao, Connor D. Martin, Hao Chen, Angel G. Garcia-Martinez, Tengfei Wang, Wenyan Han, Keita Ishiwari, Paul Meyer, Alexander Lamparelli…
    Citation: BioData Mining 2023 16:14
  3. 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