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Biomedical Literature Mining for Repurposing Laboratory Tests.

Finn Kuusisto1, Ross Kleiman2, Jeremy Weiss3

  • 1Morgridge Institute for Research, Madison, WI, USA. finn@cs.wisc.edu.

Methods in Molecular Biology (Clifton, N.J.)
|June 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a high-throughput pipeline for generating and ranking potential disease biomarkers. The approach uses machine learning and text mining to identify novel laboratory tests correlated with diseases.

Keywords:
Biomarker discoveryElectronic health recordsEpidemiologyMachine learningText mining

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Epidemiology

Background:

  • Identifying biological markers for disease states is crucial for epidemiological studies.
  • Current methods are often time-consuming, expensive, and require significant expertise, highlighting the need for efficient hypothesis generation.

Purpose of the Study:

  • To develop and present a high-throughput computational pipeline for generating and ranking high-quality hypothesized biomarkers for diseases.
  • To demonstrate the pipeline's utility in identifying novel disease-associated laboratory tests.

Main Methods:

  • Utilized machine learning models to generate a large set of candidate disease biomarker hypotheses.
  • Employed text mining to filter and rank hypotheses based on novelty.
  • Applied statistical modeling to corroborate the most promising hypotheses.

Main Results:

  • Successfully generated a ranked list of hypothesized biomarkers.
  • Identified several promising laboratory tests with previously undocumented correlations to specific diseases.
  • The pipeline integrated electronic health record data and the PubMed corpus.

Conclusions:

  • The developed high-throughput pipeline offers an efficient method for biomarker discovery.
  • This approach can significantly aid in generating high-quality, novel hypotheses for epidemiological research.
  • The findings suggest new avenues for diagnostic and prognostic marker identification.