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DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing

Weihua Guo, You (Eric) Xu, Xueyang Feng
doi: https://doi.org/10.1101/135574
Weihua Guo
aDepartment of Biological Systems Engineering, Virginia Polytechnic Institute and State University (Virginia Tech), VA
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You (Eric) Xu
bai.codes, Inc., San Francisco, CA
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Xueyang Feng
aDepartment of Biological Systems Engineering, Virginia Polytechnic Institute and State University (Virginia Tech), VA
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  • For correspondence: xueyang@vt.edu
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Abstract

Life science is entering a new era of petabyte-level sequencing data. Converting such “big data” to biological insights represents a huge challenge for computational analysis. To this end, we developed DeepMetabolism, a biology-guided deep learning system to predict cell phenotypes from transcriptomics data. By integrating unsupervised pre-training with supervised training, DeepMetabolism is able to predict phenotypes with high accuracy (PCC>0.92), high speed (<30 min for >100 GB data using a single GPU), and high robustness (tolerate up to 75% noise). We envision DeepMetabolism to bridge the gap between genotype and phenotype and to serve as a springboard for applications in synthetic biology and precision medicine.

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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 4.0 International license.
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Posted May 09, 2017.
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DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing
Weihua Guo, You (Eric) Xu, Xueyang Feng
bioRxiv 135574; doi: https://doi.org/10.1101/135574
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DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing
Weihua Guo, You (Eric) Xu, Xueyang Feng
bioRxiv 135574; doi: https://doi.org/10.1101/135574

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