Ontologies and Machine Learning Systems

  • Shoba Tegginmath
  • Russel Pears
  • Nikola Kasabov
Part of the Springer Handbooks book series (SHB)


In this chapter we review the uses of ontologies within bioinformatics and neuroinformatics and the various attempts to combine machine learning (ML) and ontologies, and the uses of data mining ontologies. This is a diverse field and there is enormous potential for wider use of ontologies in bioinformatics and neuroinformatics research and system development. A systems biology approach comprising of experimental and computational research using biological, medical, and clinical data is needed to understand complex biological processes and help scientists draw meaningful inferences and to answer questions scientists have not even attempted so far.


Gene Ontology Data Mining Domain Ontology Unify Medical Language System Data Mining Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



basic formal ontology


brain-gene ontology


chronic disease ontology


data mining


deoxyribonucleic acid


evolving connectionist system


gene ontology


gene regulatory network


intelligent discovery assistant


knowledge discovery in databases


knowledge discovery in databases ontology


machine learning


ontology of biomedical investigation


open biology ontology


ontology web language


relations ontology


Waikato environment for knowledge analysis


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Copyright information

© Springer-Verlag 2014

Authors and Affiliations

  1. 1.Computing and Mathematical SciencesAuckland University of TechnologyAucklandNew Zealand
  2. 2.Department of Computing and Mathematical SciencesAuckland University of TechnologyAucklandNew Zealand
  3. 3.KEDRI – Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

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