TY - GEN
T1 - A New Graph Database System for Multi-omics Data Integration and Mining Complex Biological Information
AU - Thapa, Ishwor
AU - Ali, Hesham
N1 - Funding Information:
Acknowledegment. This work was partly funded by the System Science Grant supported by Nebraska Research Initiative (NRI).
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Data integration
KW - Graph database
KW - Information mining
KW - Multi-omics data
UR - http://www.scopus.com/inward/record.url?scp=85090016525&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-46165-2_14
DO - 10.1007/978-3-030-46165-2_14
M3 - Conference contribution
AN - SCOPUS:85090016525
SN - 9783030461645
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 171
EP - 183
BT - Computational Advances in Bio and Medical Sciences - 9th International Conference, ICCABS 2019, Revised Selected Papers
A2 - Mandoiu, Ion
A2 - Rajasekaran, Sanguthevar
A2 - Murali, T.M.
A2 - Narasimhan, Giri
A2 - Skums, Pavel
A2 - Zelikovsky, Alexander
PB - Springer
T2 - 9th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2019
Y2 - 15 November 2019 through 17 November 2019
ER -