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Challenges of big data integration in the life sciences.

Sven Fillinger1, Luis de la Garza1, Alexander Peltzer1

  • 1Quantitative Biology Center (QBiC), University of Tübingen, Auf der Morgenstelle 10, 72076, Tübingen, Germany.

Analytical and Bioanalytical Chemistry
|August 30, 2019
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Big data, characterized by its massive volume and complexity, is transforming scientific research. This review explores its impact on bioanalytical research, offering guidelines for effective data management and utilization.

Keywords:
Big dataBioanalyticsBioinformaticsData integrationScalability

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Area of Science:

  • Bioanalytical Research
  • Data Science
  • Scientific Computing

Background:

  • Big data presents unprecedented challenges in data annotation, security, and ethical sharing.
  • Rapid advancements in data generation technology, analysis tools, and open science principles drive its emergence.
  • Disciplines beyond life sciences, like physics with the Large Hadron Collider, also grapple with big data.

Purpose of the Study:

  • To review the current state of big data in bioanalytical research.
  • To provide an overview of guidelines for the proper usage of big data in this field.

Main Methods:

  • Literature review of big data applications and challenges in bioanalytical research.
  • Analysis of technological and conceptual shifts enabling big data adoption.
  • Examination of case studies illustrating big data's impact (e.g., Higgs boson discovery).

Main Results:

  • Big data enables advanced pattern discovery, hypothesis formulation, and model development.
  • Digital biological system representations combined with machine learning open new research avenues.
  • Effective data management and ethical considerations are crucial for harnessing big data's potential.

Conclusions:

  • Big data is revolutionizing bioanalytical research, offering powerful tools for discovery.
  • Adherence to established guidelines is essential for secure, searchable, and ethically sound data practices.
  • The integration of big data principles is vital for scientific advancement across disciplines.