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A New Graph Database System for Multi-omics Data Integration and Mining Complex Biological Information

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Computational Advances in Bio and Medical Sciences (ICCABS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 12029))

Abstract

Due to the advancement in high throughput technologies and robust experimental designs, many recent studies attempt to incorporate heterogeneous data obtained from multiple technologies to improve our understanding of the molecular dynamics associated with biological processes. Currently available technologies produce wide variety of large amount of data spanning from genomics, transcriptomics, proteomics, and epigenetics. Due to the fact that such multi-omics data are very diverse and come from different biological levels, it has been a major research challenge to develop a model to properly integrate all available and relevant data to advance biomedical research. It has been argued by many researchers that the integration of multi-omics data to extract relevant biological information is currently one of the major biomedical informatics challenges. This paper proposes a new graph database model to efficiently store and mine multi-omics data. We show a working model of this graph database with transcriptomics, genomics, epigenetics and clinical data for three cancer types from the Cancer Genome Atlas. Moreover, we highlight the usefulness of graph database mining to extract relevant biological interpretations and also to find novel relationships between different data levels.

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Acknowledegment

This work was partly funded by the System Science Grant supported by Nebraska Research Initiative (NRI).

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Authors and Affiliations

  1. College of Information Science and Technology, University of Nebraska at Omaha, Omaha, USA

    Ishwor Thapa & Hesham Ali

Authors
  1. Ishwor Thapa
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  2. Hesham Ali
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Corresponding author

Correspondence to Hesham Ali .

Editor information

Editors and Affiliations

  1. University of Connecticut, Storrs, CT, USA

    Ion Măndoiu

  2. Virginia Tech, Blacksburg, VA, USA

    T. M. Murali

  3. Florida International University, Miami, FL, USA

    Giri Narasimhan

  4. University of Connecticut, Storrs, CT, USA

    Sanguthevar Rajasekaran

  5. Georgia State University, Atlanta, GA, USA

    Pavel Skums

  6. Georgia State University, Atlanta, GA, USA

    Alexander Zelikovsky

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Thapa, I., Ali, H. (2020). A New Graph Database System for Multi-omics Data Integration and Mining Complex Biological Information. In: Măndoiu, I., Murali, T., Narasimhan, G., Rajasekaran, S., Skums, P., Zelikovsky, A. (eds) Computational Advances in Bio and Medical Sciences. ICCABS 2019. Lecture Notes in Computer Science(), vol 12029. Springer, Cham. https://doi.org/10.1007/978-3-030-46165-2_14

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  • DOI: https://doi.org/10.1007/978-3-030-46165-2_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-46164-5

  • Online ISBN: 978-3-030-46165-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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