DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing
Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University (Virginia Tech), VA
arXiv:1705.03094 [q-bio.GN], (8 May 2017)
@article{guo2017deepmetabolism,
title={DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing},
author={Guo, Weihua and Xu, You and Feng, Xueyang},
year={2017},
month={may},
archivePrefix={"arXiv"},
primaryClass={q-bio.GN}
}
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.
May 11, 2017 by hgpu
