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International Symposium on Bioinformatics Research and Applications

ISBRA 2019: Bioinformatics Research and Applications pp 113–124Cite as

Gene- and Pathway-Based Deep Neural Network for Multi-omics Data Integration to Predict Cancer Survival Outcomes

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 11490))

Abstract

Data integration of multi-platform based omics data from biospecimen holds promise of improving survival prediction and personalized therapies in cancer. Multi-omics data provide comprehensive descriptions of human genomes regulated by complex interactions of multiple biological processes such as genetic, epigenetic, and transcriptional regulation. Therefore, the integration of multi-omics data is essential to decipher complex mechanisms of human diseases and to enhance treatments based on genetic understanding of each patient in precision medicine. In this paper, we propose a gene- and pathway-based deep neural network for multi-omics data integration (MiNet) to predict cancer survival outcomes. MiNet introduces a multi-omics layer that represents multi-layered biological processes of genetic, epigenetic, and transcriptional regulation, in the gene- and pathway-based neural network. MiNet captures nonlinear effects of multi-omics data to survival outcomes via a neural network framework, while allowing one to biologically interpret the model. In the extensive experiments with multi-omics data of Gliblastoma multiforme (GBM) patients, MiNet outperformed the current cutting-edge methods including SurvivalNet and Cox-nnet. Moreover, MiNet’s model showed the capability to interpret a multi-layered biological system. A number of biological literature in GBM supported the biological interpretation of MiNet. The open-source software of MiNet in PyTorch is publicly available at https://github.com/DataX-JieHao/MiNet.

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Notes

  1. 1.

    https://cancergenome.nih.gov.

  2. 2.

    https://github.com/DataX-JieHao/MiNet.

  3. 3.

    https://github.com/CancerDataScience/SurvivalNet.

  4. 4.

    https://github.com/lanagarmire/cox-nnet.

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Author information

Authors and Affiliations

  1. Analytics and Data Science Institute, Kennesaw State University, Kennesaw, GA, USA

    Jie Hao, Mohammad Masum & Mingon Kang

  2. Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Jung Hun Oh

  3. Department of Computer Science, Kennesaw State University, Marietta, GA, USA

    Mingon Kang

Authors
  1. Jie Hao
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  2. Mohammad Masum
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  3. Jung Hun Oh
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  4. Mingon Kang
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Corresponding author

Correspondence to Mingon Kang .

Editor information

Editors and Affiliations

  1. Georgia State University, Atlanta, GA, USA

    Zhipeng Cai

  2. Georgia State University, Atlanta, GA, USA

    Pavel Skums

  3. Central South University, Changsha, China

    Min Li

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Hao, J., Masum, M., Oh, J.H., Kang, M. (2019). Gene- and Pathway-Based Deep Neural Network for Multi-omics Data Integration to Predict Cancer Survival Outcomes. In: Cai, Z., Skums, P., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2019. Lecture Notes in Computer Science(), vol 11490. Springer, Cham. https://doi.org/10.1007/978-3-030-20242-2_10

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  • DOI: https://doi.org/10.1007/978-3-030-20242-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20241-5

  • Online ISBN: 978-3-030-20242-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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