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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA
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Session Introduction: Big Data Imaging Genomics.

Peter Kochunov1, Li Shen, John Darrell van Horn

  • 1Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA, pkochunov@som.umaryland.edu.

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Summary

This research explores using Big Data in imaging genomics to advance personalized medicine. It focuses on machine learning and AI to improve clinical decisions using diverse biomedical data.

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Area of Science:

  • Biomedical Informatics
  • Genomics
  • Medical Imaging

Background:

  • Translating Big Data research in imaging genomics to clinical practice presents significant challenges.
  • Integrating diverse datasets, including imaging and genomics, is crucial for advancing personalized medicine.

Purpose of the Study:

  • To address challenges in translating Big Data imaging genomics research.
  • To explore solutions for guiding individual clinical decisions using advanced data analyses.
  • To present state-of-the-art findings from large-scale biomedical datasets.

Main Methods:

  • Big Data analyses
  • Pattern recognition
  • Machine learning and artificial intelligence (AI)
  • Electronic health record (EHR) data integration

Main Results:

  • Focus on Big Data analyses and pattern recognition for clinical decision support.
  • Application of machine learning and AI to interpret complex imaging and genomic data.
  • Integration of electronic health records with imaging and genomic data.

Conclusions:

  • Advancing personalized medicine requires robust Big Data strategies in imaging genomics.
  • Machine learning and AI are key to unlocking clinical insights from diverse biomedical datasets.
  • Improved diagnostic and treatment decisions can be achieved through integrated data approaches.