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Knowledge-based biomedical Data Science.

Lawrence E Hunter1

  • 1Computational Bioscience, University of Colorado School of Medicine, Aurora, CO 80045, USA Larry.Hunter@UCDenver.edu; ORCID: https://orcid.org/0000-0003-1455-3370.

EPJ Data Science
|October 9, 2018
PubMed
Summary
This summary is machine-generated.

Computational knowledge representation and reasoning are crucial for biomedical Data Science. Integrating computational knowledge systems can enhance data interpretation and hypothesis evaluation, expanding the field.

Keywords:
Ontologyexplanationinferenceknowledge representationmachine learningreasoningtext mining

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Area of Science:

  • Biomedical Data Science
  • Computational Biology
  • Knowledge Representation

Background:

  • The US National Institutes of Health's "Big Data to Knowledge" initiative primarily focused on "Big Data," with the "Knowledge" aspect often implicit.
  • Existing research in computational knowledge representation and reasoning is highly productive but under-appreciated in Data Science.
  • Biomedical Data Science aims to create computer systems that can effectively utilize and act upon biomedical knowledge.

Purpose of the Study:

  • To highlight the importance of computational knowledge manipulation in biomedical Data Science.
  • To survey existing approaches to knowledge-based data science.
  • To argue for the expansion and application of knowledge-based approaches in the field.

Main Methods:

  • Review of computational knowledge representation and reasoning techniques.
  • Discussion of how computational systems can "act as if they knew" biomedical information.
  • Exploration of automated reasoning on computational knowledge representations.

Main Results:

  • Computational approaches can answer natural language questions, pass exams, rank hypotheses, and interpret data using prior knowledge.
  • These methods exemplify automated reasoning applied to computational knowledge.
  • The field of knowledge-based biomedical Data Science has significant potential for growth.

Conclusions:

  • Computational knowledge systems are vital for advancing biomedical Data Science.
  • Integrating knowledge representation and reasoning can lead to more sophisticated data analysis and interpretation.
  • Further research and application of these methods are encouraged to unlock new biomedical insights.