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GNE: A deep learning framework for gene network inference by aggregating biological information

View ORCID ProfileK C Kishan, Rui Li, Feng Cui, Qi Yu, Anne R. Haake
doi: https://doi.org/10.1101/300996
K C Kishan
1Rochester Institute of Technology, Golisano College of Computing and Information Sciences, Rochester, 14623, USA
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  • For correspondence: kk3671@rit.edu
Rui Li
1Rochester Institute of Technology, Golisano College of Computing and Information Sciences, Rochester, 14623, USA
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Feng Cui
2Rochester Institute of Technology, Thomas H. Gosnell School of Life Sciences, Rochester, 14623, USA
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Qi Yu
1Rochester Institute of Technology, Golisano College of Computing and Information Sciences, Rochester, 14623, USA
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Anne R. Haake
1Rochester Institute of Technology, Golisano College of Computing and Information Sciences, Rochester, 14623, USA
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Abstract

The topological landscape of gene interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, it is still a challenging task to aggregate heterogeneous biological information such as gene expression and gene interactions to achieve more accurate inference for prediction and discovery of new gene interactions. In particular, how to generate a unified vector representation to integrate diverse input data is a key challenge addressed here. We propose a scalable and robust deep learning framework to learn embedded representations to unify known gene interactions and gene expression for gene interaction predictions. These low-dimensional embeddings derive deeper insights into the structure of rapidly accumulating and diverse gene interaction networks and greatly simplify downstream modeling. We compare the predictive power of our deep embeddings to the strong baselines. The results suggest that our deep embeddings achieve significantly more accurate predictions. Moreover, a set of novel gene interaction predictions are validated by up-to-date literature-based database entries. GNE is freely available under the GNU General Public License and can be downloaded from Github (https://github.com/kckishan/GNE)

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 26, 2018.
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GNE: A deep learning framework for gene network inference by aggregating biological information
K C Kishan, Rui Li, Feng Cui, Qi Yu, Anne R. Haake
bioRxiv 300996; doi: https://doi.org/10.1101/300996
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GNE: A deep learning framework for gene network inference by aggregating biological information
K C Kishan, Rui Li, Feng Cui, Qi Yu, Anne R. Haake
bioRxiv 300996; doi: https://doi.org/10.1101/300996

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