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Next generation informatics for big data in precision medicine era.

Yuji Zhang1, Qian Zhu2, Hongfang Liu3

  • 1Division of Biostatistics and Bioinformatics, University of Maryland Greenebaum Cancer Center, Baltimore, USA ; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, USA.

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This thematic issue highlights advances in data mining and informatics for precision medicine, addressing big data challenges from genomics and electronic health records to improve healthcare delivery and biomedical research.

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Area of Science:

  • Biomedical Informatics
  • Data Mining
  • Precision Medicine

Background:

  • The convergence of data-intensive biology, informatics advancements, and evolving healthcare delivery models presents opportunities for big data analysis in biomedical research.
  • The increasing volume of data from next-generation sequencing, metabolomics, proteomics, and electronic health records necessitates sophisticated data mining techniques.

Purpose of the Study:

  • To address the data mining and informatics needs within precision medicine.
  • To showcase selected research from two international workshops focused on data mining in healthcare and translational bioinformatics.

Main Methods:

  • Organized two international workshops in 2014: Data Mining in Biomedical Informatics and Healthcare (in conjunction with PAKDD 2014) and Translational Biomedical and Clinical Informatics (in conjunction with ISB/TBC 2014).
  • Compiled and presents selected papers from these workshops in a thematic issue of BioData Mining.

Main Results:

  • The selected papers address critical data mining challenges posed by big data in biology and healthcare.
  • The issue covers advancements in handling diverse data types, including genomic, metabolomic, proteomic, and electronic health record data.

Conclusions:

  • The presented research contributes to developing knowledge systems essential for advancing precision medicine.
  • Continued development in data mining and informatics is crucial for leveraging big data in biomedical discovery and personalized healthcare.